In a world of rapid advancement and ever-shifting industry trends, continuous education and training are crucial for sustained employability. Accredify is developing a solution that can utilise technology to help corporates track the learning performance of their employees and devise necessary training interventions.

This project aims to provide a platform for our education customers to deliver a better (Learning & Development) L&D-as-a-Service experience to corporates. The platform will map courses to competency frameworks, assist corporates in identifying relevant courses for their learning & development needs and provide actionable insights with post-course evaluation. The project aims to tackle critical learners' pain points by identifying relevant courses and visualising how the additional knowledge or skills translate to improved performance.

Talent will work on post-commercialisation enhancements to the product, using implicit/explicit feedback and the user's profile to bring about more relevant recommendations.

Structural Health Monitoring (SHM) refers to the process of systematically analysing the data from existing infrastructures like skyscrapers and bridges, and extracting useful information on how they age. Such information enables predictive maintenance of critical infrastructures.

The apprentice will work closely with the Embedded and Hardware team, to develop and improve the current radio-frequency (RF) communication protocols and applications. They should take the initiative to see the product through the entire lifecycle from proof-of-concept to design and production.

Structural Health Monitoring (SHM) refers to the process of systematically analysing the data from existing infrastructures like skyscrapers and bridges, and extracting useful information on how they age. Such information enables predictive maintenance of critical infrastructures. We are seeking an enthusiastic individual to contribute to our SHM development efforts.

The apprentice will provide Research & Development assistance for the 4th Generation series of  Ackcio products.

Social media is ubiquitous and ever-evolving. Billions of people consume content on platforms like Instagram, TikTok, Twitter every day. Behind this enormous amount of data is a wealth of information about the world and its people, waiting to be unlocked.

Affable uses Deep Learning to analyse social media content at scale. They have analysed over 2 billion+ posts and 500 million+ public accounts via cutting edge NLP and Computer Vision models. To unlock the world's social media data is the mission of Affable, and the startup is looking for an apprentice to join them in that mission.

Affable uses Deep Learning to analyse social media content at scale. They have analysed over 2 billion+ posts and 500 million+ public accounts via cutting edge NLP and Computer Vision models. To unlock the world's social media data is the mission of Affable, and the startup is looking for an apprentice to join them in that mission.

This project is about researching, training and deploying new machine learning models to glean more insights from our dataset. This includes:

1. Location prediction of social media users via deep learning
2. Gender prediction of social media users via deep learning
3. Identification of same user across different social media platforms

The objective of this project is to understand the consumer sentiment for a given topic in the FMCG sector and generate engagement materials (ads, product concepts, etc.) to increase consumer engagement for a given topic.

In order to identify a consumer conversation precisely, they need to understand the semantics in the conversation, detect entities and topics, and understand the sentiment of those topics. Once these are being identified, they need to create conversation materials such as ads, tag lines, product concepts that are closer to the consumer conversations. They need to do this at scale to keep up with the fast-changing consumer world today. This will be challenging because of the multiple languages spoken in different regions.

The project will develop productivity-driven insights for the construction site team through deep-learning computer vision techniques.

CCTVs are present at almost all construction sites and are mainly used for manual progress monitoring or reviewing past footage if something goes wrong. The information will be tracked through the existing CCTVs at the sites to give the project team meaningful insights and knowledge to understand progress and identify potential risks early on. It is still a massive challenge for companies to understand their progress on a macro level as it is hard to make sense of all the information from so many different subcontractors. This results in a lack of visibility of issues on-site that can cause delays in concreting works due to the underutilisation of the assets.

The project attempts to build state-of-the-art AI solutions and automation systems for the built environment. The candidate will work alongside under guidance of a chief scientist to develop AI solutions in a few live sites for human activity recognition (HAR). We are looking for talent to speed up the development of appropriate AI models for more accurate defects and HAR with F1 recall greater than 90%. We are looking for ML engineers with a systematic mindset, willing to work at length to create solutions that never existed before.

At Allozymes, we require the ability to read DNA molecules that are much larger than 150-250 bp (the range of short-read sequencing method), to allow us to analyse the distribution of mutations during the enzyme or strain engineering processes. This helps us to detect the mutants that provide better performance of an enzyme than the original and enables us to reproduce it.

Although we have established some in-house sequencing capabilities for the sequencing of bulk selected droplets to accelerate our developments for enzyme and strain engineering, more work is required for the development of selected droplets sequencing in a plate-based format. The current project aims to utilize Nanopore technologies and a liquid handler to sequence the selected droplets that are separated individually into each well. By the use of a liquid handler, we can automate the tedious process of extracting and amplification the DNA, before combing the samples for Nanopore sequencing preparation, to allow us to fine-tune the enzyme and its engineering process. In addition, Allozymes is using a proprietary droplet microfluidic system for enzyme engineering and subsequently, the DNA molecule is present in tiny amounts which further optimization is required to employ these sequencing technologies.

In this project, the apprentice will be extracting and preparing the samples for sequencing. The apprentice would be tasked to extract and handle multiple DNA samples, maintain the quality of the samples, and prepare the samples for sequencing. The apprentice will also be working with a liquid handler to streamline the sequencing process of processing hundreds of samples at a given time. This project will require collaboration with other scientists and engineers in the company. The outcome of the project will enhance the performance of the Allozymes proprietary enzyme engineering platform.

Carotenes are long-chain hydrocarbon molecules commonly found in the pigments of plants. These molecules play an important role in the life chain, such as photosynthesis. Phytoene performs the role of UV protection, antioxidants, and anti-inflammatory, and the cosmetic industry has started benefiting from it in its skin care products.

However, producing it from natural sources is resource intensive and costly and it takes 10,000kg of tomato skin to extract about 3kg of phytoene. Additionally, customer behaviour has significantly changed, and they demand ingredients from natural sources, which makes chemical synthesis approach less attractive for the cosmetic companies. Therefore, there is a need for biosynthesis of phytoene and similar compounds.

In this project, the apprentice will employ enzyme and strain characterisation and optimisation techniques to identify suitable enzymes and improve the bioprocess yield. The apprentice will need to conduct screening and characterisation of enzymes, improve the yield of production and optimise the bioprocess conditions.

Allozymes focus is on the rapid evolution of optimised enzymes with improved performance, revolutionising the way industries manufacture complex natural products. The company’s proprietary platform enables us to rapidly screen through thousands of enzyme variants for specific substrate activities, generating both genomic and analytical data. This data is large and requires us to establish efficient workflows for its management and usage. As a first step, Allozymes built a cloud-based data system that integrates into our current experimental data management system and allows streamlining data storage and analysis.

For the next step in this process, this data system will be used to analyse and build workflows for data processing, data analysis and to perform predictions using artificial intelligence approaches, such as Machine Learning and Deep Learning.

In this project, the apprentice will work on the development, implementation and deployment of artificial intelligence solutions aiming at improving Allozymes' current enzyme evolution platform. The apprentice will be trained to process and analyse data, implement machine learning algorithms, and deploy machine learning pipelines using real genomic and experimental data.

This project will require collaboration with the scientists and engineers in the company to achieve the proposed goals. The outcome of the project will enhance the performance of the Allozymes proprietary platform for engineering enzymes and microorganisms.

Current industry of Cultivated meats is mostly built on the concept of a hybrid between cell mass and plant-based scaffolds.

Ants Innovate approaches this with a two-prong approach of extracting the unique meaty flavour of cells and developing textured plant-based formulations.

This project looks at the design of protein structuring and oil-based solids to create a prototype that replicates the appearance and mouthfeel of a conventional meat cut.

Current industry of Cultivated meats is mostly built on the concept of a hybrid between cell mass and plant-based scaffolds.

Ants Innovate approaches this with a two-prong approach of extracting the unique meaty flavour of cells and developing textured plant-based formulations 

This project looks at the design of the textured plant-based formulations and the optimization of the incorporation of the cell products to create a prototype that replicates the taste, appearance and mouthfeel of conventional meat.

Atomionics is working on quantum sensors, based on cold atom interferometry, where atoms are cooled down to almost absolute zero using lasers.

At Atomionics, you will get the chance to prototype, test, refine, finalise, and produce your designs toward making our devices compact and robust for field deployment with the only constraints being power and your imagination.

As part of the laser optical system of the quantum sensor, a DDS (Direct Digital Synthesis) is used as a waveform generator to control phase and frequency of the signal waves. This is controlled by an FPGA and requires firmware to be written in VHDL.

Atomionics is working on quantum sensors, based on cold atom interferometry, where atoms are cooled down to almost absolute zero using lasers. At such cold temperatures, these atoms start behaving like waves, which allows us to measure the forces acting on them precisely.

This opens up a wide range of applications including, but not limited to, paving a path in the energy transition to zero-carbon, exponentially improving the performance of inertial navigation systems, and transforming the global positioning system. At Atomionics, you will get the chance to prototype, test, refine, finalise, and produce your designs toward making our devices compact and robust for field deployment with the only constraints being power and your imagination.

Our gravimeter comprises hundreds of components, including a titanium vacuum chamber, optical laser systems, and actuating mechanisms. This is combined with exotic materials, micron-scale tolerances, and complex assemblies. The project involves the continued mechanical engineering development of our machine, and its subsystems, to improve performance, reliability, quality, and reduce assembly costs through creative design and application of engineering principles.

Atomionics works on quantum sensors based on cold atom interferometry, where atoms are cooled down to almost absolute zero using lasers. At such cold temperatures, these atoms start behaving like waves, which allows us to measure the forces acting on them precisely.

This opens up a wide range of applications including, but not limited to, paving a path in the energy transition to zero-carbon, exponentially improving the performance of inertial navigation systems, and transforming the global positioning system. At Atomionics, you will get the chance to prototype, test, refine, finalise, and produce your designs toward making our devices compact and robust for field deployment. The only constraints are power and your imagination.

Our gravimeter has an electronics system comprising dozens of custom design PCBs used for everything, from power distribution, power sequencing, data signal processing, waveform generators, and filtering to radio frequency devices to control quantum components. This project is to develop and refine these PCBs to improve performance and reduce complexity, size, and cost.

Atomionics is working on quantum sensors, based on cold atom interferometry, where atoms are cooled down to almost absolute zero using lasers. At such cold temperatures, these atoms start behaving like waves, which allows us to measure the forces acting on them precisely.

This opens up a wide range of applications including, but not limited to, paving a path in the energy transition to zero-carbon, exponentially improving the performance of inertial navigation systems, and transforming the global positioning system. At Atomionics, you will get the chance to prototype, test, refine, finalise, and produce your designs toward making our devices compact and robust for field deployment with the only constraints being power and your imagination.

The project objective is to optimise SWaP (size, weight, and power) for a cold atom trap for atom interferometry application. The successful candidates will work with other senior physicists and mechanical engineers at Atomionics to design and build a miniaturised version of a magneto-optical trap (MOT), which can cool down atoms to micro-Kelvin temperature, taking into account required optical beam access for measurement. Design optimisation efforts will focus on the process of laser cooling of atoms, magnetic field calculations for an optimal trap configuration with anti-Helmholtz coils, and optical beam configuration for the measurement of gravity using atom interferometry in an ultra-high vacuum chamber.

Robot development is currently a fragmented and difficult process. This is particularly the case for dynamic automation development where AI and computer vision systems have to be integrated.

The apprentice will be a part of Augmentus's technical team responsible for developing a fully integrated and offline robot simulation environment that allows users to program industrial robotic systems in minutes instead of months. The apprentice will also develop robot programming features that are important for robotic development across various industrial applications.

Cellular agriculture has been one the important innovations in clean meat revolution and is one of the fastest growing sectors in biotechnology. Scaling up cells from tissue biopsies to 2D and 3D cultures for producing meat a.k.a. cultured meat is not so effective due to multiple factors. Culturing cells efficiently from tissue biopsies, lack of cost effective edible microcarriers for scaling up to produce cultured meat in the bioreactor has not been addressed to its entirety by cell based meat manufacturers.

Cellivate Technologies addresses these gaps through the use of proprietary coating (ProCyte) on 2D surfaces as well as 3D edible microcarriers for producing cell-based meat products. Their ProCyte surfaces increase yield 2x to 4x and edible microcarrier prototypes have been well received by their customers.

Apprentice will support their ongoing developments on novel edible microcarriers and the scaling up of Cellivate's proprietary coatings on cell culture plastic wares.

Immune cells derived from the patient are isolated (Autologous Cells) have been genetically engineered and re-infused to patients with promising immunotherapies outcome. However, patient-derived cells are normally accompanied by a certain extent of functional impairment, senescence or exhaustion from prior treatment that led to a reduction in the potency of the products.  We are developing differentiated, off-the-shelf CAR-T cell therapies, derived from healthy donor T cells (Allogeneic Cells).  These genetically modified cells could serve patients immediately, and enable an overall efficient manufacturing process leading to reduction in cost of goods.

The key challenges faced in the development and generation of allogeneic CAR-products is the eliciting the graft-versus-host disease (GVHD) and allo-rejection in addition to durability of the products. The aim of this project is to address the above issues in CAR-T cell design and manufacturing. Specifically, we will explore various sources of immune cells including healthy donor derived Peripheral blood mononuclear cells, Cord blood mononuclear cells, Natural killer cells or Gamma-delta T cells, as well as different strategies that could potentially enhance the overall efficiencies of the allogeneic cellular platform.

Curium drives several technologies capable of performing rapid and automatic calibration of sensors in fully automated systems such as autonomous vehicles.

Currently, Curium is deploying increasingly advanced technologies into vehicles that are creeping up the level of autonomy from Level 2 to Level 4. This project aims to enhance these current systems and elevate these manufacturers to deploy Level 5 autonomous vehicles.

The current problem is that sensors can become miscalibrated for any number of environmental reasons. With these sensors providing erroneous reading, the decision-making algorithms will make incorrect decisions leading to injury or death to vehicle owners, occupants, or pedestrians.

By monitoring and automatically calibrating the sensors in real-time, we can improve uptime and reduce the likelihood of miscalibrations leading to a dangerous situation.

The apprentice will work with the Curium Development Team to develop novel techniques to measure, monitor, and adjust sensor data in near real-time for Automotive use-cases. The challenges are to problem-solve the issues related to modelling 3D environments in a highly efficient manner to leverage the limited computational resources that are typically embedded into onboard vehicle systems. 3D environments will be modelled primarily using Inertial Measurement Units (IMUs), LIDAR, RADAR, and Camera sensors and deployed on computational platforms such as (but not limited to) the NVIDIA platform.

DiMuto is a rising Agri Fintech company based in Singapore. We simplify every step of global AgriFood trade. From produce, to trade, to market, our AgriFood Trade Solutions help growers, exporters, and importers to trade efficiently with better visibility and finance.

One of the key parts of their solution is a Digitization Device (DACKY). The team is looking for someone to assist in helping to improve and refine the devices design, and manufacturing process, and also work together with the technical team to deploy AI to read real-time images on DACKY devices at our customer sites - basically how we can take better pictures, add just-in-time printing capabilities, deploy AI, deploy computer vision etc. The company is looking to make the DACKY more multi-purpose according to our various customer requirements so that it can better suit the operational demands and environment.

The apprentice will learn and develop Quantum Computing approaches to real-world discrete optimisation problems. The main goal of the project is to bridge the gap between the most prevalent techniques in Optimisation, Machine Learning and near-term Quantum Computing.

The apprentice will be contributing to Entropica Labs' core software technology and participating in customer projects. You will analyse models and suggest implementations on Quantum computers while benchmarking the performances using standard and proprietary datasets. You will also get the opportunity to work directly with our customers, assisting them with testing and implementing optimisation workflows.

The apprentice will be developing numerical methods for Machine Learning and Optimisation Theory to enhance the current capabilities in Quantum Computing and contribute to Entropica Labs' core software technology and participate in customer projects.

The main goal of this project is to test advanced methods for Machine Learning to optimise Quantum computers' performances and benchmark Entropica's Optimisation Solvers by running benchmarking experiments using advanced annealing-based solvers.

In this project, the apprentice will be part of the core development team and will focus on the algorithmic design, development and deployment of 2D/3D Computer Vision applications based on classical methods as well as Deep Learning. The apprentice will work on projects at various stages including development and implementation of future robotic perception capabilities, as well as troubleshooting and improvement of robotic perception pipelines in systems currently deployed at project site (80% onsite required).

In this project, the apprentice will be part of the core development team focused on deploying our autonomous grouting robots in big spaces like shopping malls or airports.

The work will build on top of the single-robot logic used for grouting apartments. The apprentice can expect to work on a subset of creating new simulation scenarios, making GPU accelerated simulations, developing distributed organization logic, ROS2 or Arduino code, figuring out user-robot interactions, enhancing deep computer vision models with newly collected data including deployment.

There will be an opportunity to take ownership of work and be involved in testing the contributions on construction sites.

Breast cancer is the most commonly occurring cancer among women in Singapore. Early Screening and detection are crucial for helping to detect these cancers at the early stage, where the survival rate is significantly higher. However, current screening procedures have some deficiencies, including about 15-20% of cases being false recalls which leads to unnecessary anxiety and 10% of cases being false negatives resulting in missed interval cancers and opportunity for early diagnostics and treatment. In addition, the existing screening procedure requires 2 readers in a double-blind reading setting and takes a significant amount of time.

Solution: FxMammo and FxTomo are AI Assistants that help to significantly increase the accuracy of breast cancer screening. They reduce false positive rates, missed interval cancers and also enhance the screening procedure by cutting down the time taken by helping radiologists to make better judgement and increasing the capacity of radiologists to handle more work.

The project is within SpaceChain's Decentralised Satellite Application (DSA) platform, focusing on satellite applications and satellite services crowdsourcing.

The target is to improve the accuracy of various machine learning models that the team has previously developed for analysing earth observation data. The models include land classification and object detection.

Once the collaborative environment of the DSA is constructed, the DSA platform will transform into an open-source project led by SpaceChain and contributed by developers. So further development includes model deployment and endpoint construction.

We are a fast-growing company, and we believe that the sustainable way to keep this growth is by educating online sellers via various digital content and free-to-use tools. At Konigle, we simplify the process of running an e-commerce store for the sellers and defining how to run and grow a store profitably. We are doing this by creating a suite of various productivity tools, automation and shortcuts for multiple e-commerce platforms, store builders, and many more.

Apollo is an eCommerce intelligence engine developed by the Konigle core team. It is the brain on which all of Konigle's intelligence decisions are made for our customers. Now, this is used by over 1,000+ customers in over 60+ countries. We're constantly adding capabilities to the brain. This involves coming up with insights such as forecasts, suggestions, reporting and many other functionalities. We want mentees to experience this firsthand, understand the system, and start adding functionality to it.

We are a fast-growing company, and we believe that the sustainable way to keep this growth is by educating online sellers via various digital content and free-to-use tools. At Konigle, we simplify the process of running an e-commerce store for the sellers and defining how to run and grow a store profitably. We are doing this by creating various productivity tools, automation and shortcuts for multiple e-commerce platforms, store builders, and many more.

Apollo is an eCommerce intelligence engine developed by the Konigle core team. It is the brain on which all of Konigle's intelligence decisions are made for our customers. Now, this is used by over 1,000+ customers in over 60+ countries. We're constantly adding capabilities to the brain. This involves coming up with insights such as forecasts, suggestions, reporting and many other functionalities. We want mentees to experience this firsthand, understand the system, and start adding functionality to it.

Aerogel materials can deliver thermal insulation performance several times better than conventional materials. Despite their great potential, slow commercialisation of aerogels can be traced to their expensive and energy-intensive manufacturing process, inherent dust and fragility challenges.

KrossLinker’s patented aerogel technology addresses the above challenges with a breakthrough fabrication process and proprietary green formulation to produce high-performance, robust, lightweight, thinner and cost-competitive aerogel insulation material.

This project involves applying material chemistry knowledge to design and develop new aerogel formulations and optimise their proprietary fabrication process for commercial thermal insulation applications in EV and high-temperature applications. This project would play an essential milestone in the formulation and fabrication of this advanced material.

This project is established to develop AI/NLP language learning solutions that are modified to enhance the learning experience of users from a particular cultural context.

Lalia has already begun collecting data from various markets and it will be used in this project by the apprentice to support the team in refining models and systems. The focus of the project would be to build systems and models that allow Lalia to provide instantaneous, high-quality feedback in a way most suited and understood by the targeted learner's cultural context.

To understand the narrative of what a person might be doing in a physical space, they utilise RE-ID systems integrated into an end-to-end action capture system to leverage video images for pattern-of-life analysis and full body image capture. The data collected will be processed to identify shopper demographics and intentions using graph analytics, behaviour capture and reinforcement learning-based demographic classification-based scoring.

The apprentice will be working on building Deep Learning pipelines, analytical capabilities and machine learning models to run across these datasets for clients like the Singapore and US governments.

Everyday around the world people go blind from not having access to an eye specialist. Their technology brings eye screening to primary care, a paradigm shift needed to end preventable blindness. Using their AI technology, all doctors are empowered to grade fundus images and detect diabetic retinopathy, even without a specialist.

The apprentice will own a Deep Learning project from beginning to end. He/She will be responsible for defining an implementation strategy after the literature review, implementing the strategy in consultation with medical consultants and assisting in the deployment and validation of the solution.

MicroSec empowers organisations to take control over their environment through our unique approach of Security by Design at the Edge for IoT, IIoT and OT devices and networks. This is done by proactively preventing attacks from the inside-out, stopping zero-day attacks on devices, preventing device and network intrusions, creating chains of trust between devices, and reducing your operational risks and costs starting from the device to the cloud.

MicroSec provides enterprise-grade PKI (Public Key Infrastructure) solutions to IoT devices. We also use our patented microcerts instead of traditional X509 certificates which are more amenable to being transported over low bandwidth protocols like BLE, NB-IoT etc.

However, some users may not be satisfied with this level of security with concerns about the power of quantum computing algorithms. In this case, one would need to rely on PQC (post-quantum cryptography) based techniques for key establishment and QKD (quantum key distribution) based techniques for key distribution.

The challenge in applying PQC algorithms in the IoT space is not limited to considering space and time requirements. Key and ciphertext sizes must be kept within limits to effectively transport them via low bandwidth communication protocols like LoRaWAN, BLE, and NB-IoT.

The objective is to arrive at and implement a cryptographic scheme that has a reduction to a known NP-hard mathematical problem that is not solvable in polynomial time using any known quantum algorithms which are comparable to current ECC implementations on 32-bit ARM processors in terms of speed, memory requirements and code size with keys and ciphertext which can be effectively transported over low bandwidth protocols like LoRaWAN, Bluetooth, and NB-IoT.

MicroSec empowers organisations to take control over their environment through our unique approach of Security by Design at the Edge for IoT, IIoT and OT devices and networks, from proactively preventing attacks from the inside-out, stopping zero-day attacks on devices, preventing device and network intrusions, creating chains of trust between devices, and reducing your operational risks and costs starting from the device to the cloud.

MicroSec provides enterprise-grade PKI (Public Key Infrastructure) solutions to IoT devices. We also use our patented microcerts instead of traditional X509 certificates which are more amenable to being transported over low bandwidth protocols like BLE, NB-IoT etc.

However, some users may not be satisfied with this level of security with concerns about the power of quantum computing algorithms. In this case, one would need to rely on PQC (post-quantum cryptography) based techniques for key establishment and QKD (quantum key distribution) based techniques for key distribution.

The challenge in the application of PQC algorithms in the IoT space is not only the consideration of space and time requirements but also ensuring that the key and ciphertext sizes are kept within limits such that they can be effectively transported via low bandwidth communication protocols like LoRaWAN, BLE, and NB-IoT.

The objective then is to arrive at and implement a cryptographic scheme that has a reduction to a known NP-hard mathematical problem that is not solvable in polynomial time using any known quantum algorithms which are comparable to current ECC implementations on 32-bit ARM processors in terms of speed, memory requirements and code size with keys and ciphertext which can be effectively transported over low bandwidth protocols like LoRaWAN, Bluetooth, and NB-IoT.

With the expansion of Nanolumi’s technology portfolio in lipid encapsulated fluorescence nano particles, the team aims to venture into offering novel imaging solutions to life sciences applications.

In this project, team members would be developing and evaluating the suitability of the new class of novel lipid encapsulated fluorescence nanoparticles in its key performance metrics against commercial products. As well as develop strong use cases for advance imaging techniques like Fluorescence lifetime imaging (FLIM) and two photon imaging.

Also, new surface functionality would be explored for the nanoparticles and evaluation of the materials with live cells required.

Reyal™ is a new, cloud-based, multi-layer solution with the technology and tools for companies to connect every physical product with a unique digital identity for secure product authentication & counterfeit protection. The Reyal™ solution combines the company's multi-disciplinary knowledge in advanced material science, photonics and electronics, as well as marketing.

With the solution expansion and product development, the authentication process and system need to be upgraded continuously, with the help of advanced signature materials development, deep learning, AI-based detection and analysis mechanism, data mining from the cloud database, pattern recognition and analysis through new methodology development (multispectral imaging, analysis software, and hardware integration, etc.).

A key area of the project would be to develop a new generation solution, such as but not limited to, unique anti counterfeit signature materials, AI-based detection and analysis mechanism, as well as a B2C user-friendly AI-based hardware/software (smartphone version reader/applications) which allows readout and analysis of unique and sophisticated spectral signature and/or images, or combination of any those technologies.

At the same time, within the Reyal broad project scope, a secondary task will be to improve and expand the Reyal cloud database in features, such as interface, dashboard, auto-monitoring, data auto-processing and reporting.

For the project, we aim to design and develop a 6 degrees of freedom motor controlled robotic system for X-ray and CT image-guided systems. We will first determine the parameters for the robotic application, then design the robotic mechanism, develop.

In the robotics design phase, it involves using 3D modelling software for conceptual and detailed design of robot components and motion control.For this project attachment, the selected candidate will get the opportunity to work with experienced mechanical engineers, software engineers and AI experts during the system integration.

Intensive farming and unregulated usage of antibiotics in agriculture has led to development of antibiotic resistance that spilled over to human population, creating healthcare burden worldwide.

Peptobiotics has identified this issue and aims to develop recombinant livestock feed additives as replacements for agricultural antibiotics and hormones.

Peptobiotics is a seed funded startup that is currently in the stage of scaling up our production based on promising antibiotic alternatives that we have discovered.

In this project, the apprentice will apply their synthetic biology and molecular biology knowledge to engineer our Anti-Microbial Peptide (AMP) producing strains to reach higher productivity.

As most of the microscopes are standalone machines and used by an operator in an offline condition, this project requires the microscopy to be connected online live and be operated remotely. The remote access and control can be through web pages or AR/VR systems. The talent will be involved in the IoT development of microscopy.

The objective is to build up the IoT ego system to prepare the microscopy for future Metaverse applications. It is not common in the industry to connect the microscopy or remotely access it through the internet. Synchronising the operation of the scope without crashing into the sample is one big challenge.

One key function of microscopy is acquiring images. This is very time-consuming, from image acquisition to the analysis of the images. We have already automated the process of image acquisition. But there are still many improvements in image processing, analysis, and decision-making. Therefore, deep learning, pattern matching, and AI will help reduce the processing time by cutting down the manual work. The challenge is to build a system that allows us to speed up the image processing and deep learning to speed up the process between different types of samples.

Leading the advanced optical industry, Phaos’ optical Microsphere Nanoscopy (ONM) technology has achieved a breakthrough in optical and electron microscopy. The resolution of an optical imaging system has a principal limitation to resolving two adjacent objects due to the physics of diffraction. With the control of light’s wavelength and assistance by the microsphere, OMN can resolve target features down to 137 nm, which is way below the physical diffraction limit of 200 nm under visible light. This is made possible using simulation software, DOE (diffractive optical element), and the optical system’s physical design.

This project is to conceptualise and design amicrosphere to assist microscopic products that will be able to resolve two adjacent objects up to the size of 50nm using simulation software, DOE and physical design of the optical system.

Polybee is building autonomous drone solutions for pollination in indoor greenhouse farming. We have developed a novel patent-pending method of pollination with drones called Aerodynamically Controlled Pollination. This is a bio-inspired approach; the turbulent wake of drones is optimized to vibrate the flowers at just the right frequencies, much like the bumblebees when they buzz-pollinate. With this method, we can pollinate tomatoes, pepper, eggplant, and strawberries: the biggest crops in the greenhouse sector. Our core pillars of technology are automation of micro-drones, computer vision for perception and analytics, and a software platform for deployment and visualization. In this project, you will be involved in development of computer vision pipelines to do crop measurements and liaise with our robotics and horticulture teams.

As a computer vision intern at Polybee, you will be working on developing and deploying deep learning and image processing pipelines for high throughput phenotyping. You will be using data from a multitude of sensors (stereo-camera/NIR/thermal/climate) to derive valuable insights on plant morphology, plant health and detect stress, diseases, etc. This involves translating cutting-edge research into cost-effective, scalable industry solutions. If working on interesting real-world problems in a fast-paced multi-disciplinary team excites you, Polybee wants to hear from you!

In this project, you will be involved in deploying autonomous micro-drones in greenhouses, and liaise with the computer vision and software teams.

As an aerial robotics apprentice at Polybee, you will find yourself in various dynamic and high-impact situations; one day you could be writing software to make a micro-drone fly itself to a flower and perform pollination, on another day you might be developing a mobile app to help their users interact with their robotic bees. You will have the opportunity to take ownership of your work and be involved in deploying your own contributions at their customers' sites.

With the current process of polymer development, development scientists draw on their material knowledge to formulate new compound recipes, mix small batches on trial and test and then repeat the loop until the desired material properties are achieved, resulting in countless development hours. Complex recipes can take more than a year to develop.

Polymerize enables R&D scientists to reduce the trial & error in formulation development with a complete product lifecycle management software that will allow companies to efficiently access/share data and maintain formulation workflow orders, applications recommendations, reporting and analytics.

The MLOps role is a challenging, steep, and hands-on role for individuals ready to take full responsibility for the AI/ML applications within the Polymerize platform. The goal is to ensure the platform runs seamlessly with maximum automation and minimum supervision. We plan to achieve this by building a highly scalable, consistent, and seamless workflow with strong technical expertise, the latest technologies and state-of-the-art algorithms for ML and AI. We work as a closely knit team with no hierarchy and a constant push for ownership and customer-centric delivery of responsibilities. 

This role is also about having a lot of fun brainstorming, sometimes profoundly enlightening discussions on common industry problems, technology limitations, domain-specific roadblocks, and applications of AI. We strongly vouch for flexibility, deep work, and a balance of learning something you don't know while teaching something you know for the team's collective growth.

Ocean transportation data is segregated across multiple sources like ocean carriers, satellite, ports, etc. In order to reach exceptional data quality, Portcast has set up processes to deep dive into completeness, correctness and accuracy of the data at each step of the ocean movement.

The Data Science trainee will work with cross-functional teams (data science, software development and business) to identify areas where model features can be improved to deliver better accuracy and granularity in the event of contingencies.

This project will focus on biomechanical analysis for clinical applications, more precisely on the evaluation of the knee joint movement for a better understanding and diagnosis of knee joint injuries and pathologies.

GATOR by PreciX is used to collect 3D Knee kinematics data during various movement activities by patients. This time series data is multidimensional and complex which demands use of ML based techniques for analysis.

The main purpose of this study is to develop algorithms to analyse altered movement patterns in knee osteoarthritis (OA) patients. Understanding kinematic patterns in patients with functional impairments will help early diagnosis of OA and develop treatment options.

This will include identifying distinguishing features in the clinically referred movement exercises performed by the subjects and successful training machine learning models for diagnosis. Precix has a library of biomechanics datasets with different movement profiles to be used during the project.

PreciX is a MedTech startup focused on developing a novel wearable device for the knee joint. This device aims to provide a range of biomechanical measurements, by leveraging a modular and scalable multi-sensor platform. The clinician will use these measurements to accurately assess the functional performance of the knee joint during natural dynamic movement, thereby enabling a range of clinical applications such as screening, diagnosis support, and recovery tracking.

The project will focus on biomechanical analysis for clinical applications, more precisely on the evaluation of the knee joint movement for a better understanding and diagnosis of knee joint injuries and pathologies. The main purpose of this study is to develop algorithms to analyse altered movement patterns in ACL injured patients.

RoPlus aims to help local companies automate their production lines for more innovative and faster production processes by providing automated gripping solutions with computer visioning. Our research experience in robotics and computer vision allows us to provide feasible and effective solutions for automation challenges in different industries. Currently, we are expanding our team for our product launch.

The robotics engineer is responsible for the evaluation of our gripper products with industrial partners, conducting durability tests to come to our product specifications, and supporting product developments for customisation projects at RoPlus. The apprentice should have experience in using open-source object detection algorithms or ROS motion planning API, and in dealing with industrial robotics systems. Fresh graduates with robotics experience are welcome to apply for the role as well.

RoPlus aims to help local companies automate their production lines for more innovative and faster production processes by providing automated gripping solutions with computer visioning. For the past three years, our R&D team has been focusing on developing soft grippers, exploring various automated solutions for production lines or retail distribution centres, and integrating computer vision in pick and place tasks. Our research experience in robotics and computer vision allows us to provide feasible and effective solutions for automation challenges in different industries.

RoPlus provides intelligent reconfigurable gripping solutions to increase packaging efficiency. Our technology is expected to impact a range of industries, including but not limited to food assembly, vertical farming and fast-moving consumer goods packaging. Currently, we are expanding our team for our product launch. The apprentice will be a part of RoPlus's technical team responsible for developing standard protocols for validating product characters and providing proposals on higher repeatability results and quality management. In the end, we aim to have the apprentice monitoring our product performance prior to the official delivery to the client or to the next stage of integration.

The apprentice will architect a new data system from the ground up, from establishing a data pipeline to building statistical and predictive models to process polymer research and operations data. Leveraging data science tools and machine learning frameworks, the apprentice will add significant value to their membrane and digital solution products by enabling both internal users and customers to monitor performance, extract operational insights, and enhance overall production yield and quality.

Seventh Sense builds essential technology and products to bring ultra-efficient AI computer vision to the edge. We believe that billions of sensors, i.e. edge devices, such as cameras, etc. expected to come online in the coming decade. There is a compelling need for these devices to see, listen, reason, and predict without constant connectivity to the cloud or servers (i.e. Edge-Based AI).

At Seventh Sense, we are solving the problem of running computationally intensive deep learning (AI) without needing expensive GPUs. We enable GPU-class machine vision performance on commoditised hardware such as ubiquitous CPUs, mCPUs, embedded systems, and compute-constrained devices.

Our product focus is twofold – provide API/SDK product for developers and companies building their bespoke solutions, and ii) build our own software-first surveillance and security solutions. Aside from building the core technology, we are focused on using our technology to build products for the defence, security, and surveillance industry. We develop customised facial recognition algorithms depending on the hardware, including low-power low compute hardware.

The project aims to design, develop and customise the computer vision model so that it can be used on specific hardware, which will then be commercialised. The apprentice will work closely with the AI engineers to develop the optimum model for the hardware.

To launch a constellation of satellites to enable worldwide entanglement distribution, serving as the communication backbone for the quantum internet.

The apprentice will be supporting the R&D of space-based Quantum Entanglement instrumentation at SpeQtral, including entanglement generation onboard a nanosatellite and optimising quantum instrumentation. This can cover both hardware and software development efforts.

SpeQtral is commercialising quantum communications to prepare the world for the quantum future. Think quantum space lasers, entanglement and military level secure communications. As part of our mission to transform the world's networks, we plan to launch a constellation of satellites to enable world-wide entanglement distribution. The earliest application of this was the highly secure delivery of encryption keys to facilitate secret communications.

The delivery of secret keys via quantum entanglement (or quantum key distribution, QKD) originates at a physical level, where light particles travel from one location to another and are detected by extremely sensitive detectors. However, once the light particles are exchanged and detected by the communicating parties, the extraction of the information that makes up the secret key is entirely reliant on software methods. In the QKD field, the necessary software to deliver the secret keys is known as the QKD software stack. From signal processing challenges (i.e. detection of correlation peaks in high-loss environments) to optimisation of security (i.e. implementation of error correction codes or privacy amplification) the QKD stack presents several challenges that need to be addressed in order to bring entanglement-based QKD from academic levels to commercial maturity.

To launch a constellation of satellites to enable worldwide entanglement distribution, serving as the communication backbone for the quantum internet.

The apprentice will be supporting the R&D of space-based Quantum Entanglement instrumentation at SpeQtral, including entanglement generation onboard a nanosatellite and optimising quantum instrumentation. This can cover both hardware and software development efforts.

SunGreenH2 manufactures core components for electrolyser cells, stacks and systems, enabling our customers to produce affordable green hydrogen. We use our platform technology to incorporate proprietary advanced nanostructured materials into electrolyser components. Electrolysers made with our components dramatically increase production and decrease energy consumption with 30x reduction in the use of expensive platinum group metals.‍

Our novel, breakthrough technology results from over 10 years of cutting-edge research and innovation in electrochemistry and nanotechnology for renewable energy generation. We have a vision for zero carbon, low-cost, green hydrogen available globally at scale.

Tigtag's mobile application supports drawing squares and polygons around objects in images to label them. There is a current challenge to do polygonal bounding boxes when points are added one at a time around the border of the object.

In this project, apprentices will explore developing a new feature where the user highlights a square area around an object, and a resulting polygon around the object is automatically generated with points on the image.

This will require an object detection algorithm to be built with points plotted with high level of accuracy around the object. This feature will be game changing for the company as we are a mobile first company with no other players in the market is currently doing this to achieve economy of scale for drawing polygonal bounding box to power computer vision projects.

Today, most countries worldwide lack access to the internet and the resources to do business, exchange ideas and learn. At Transcelestial, we are building a space laser communication network to deliver the future of Internet Distribution worldwide. This will be the fastest way to bring the rest of the world up on the bandwidth and connectivity curve.

Laser-based communication technology has the potential to provide power-efficient communication with very high bandwidth. These benefits are achieved by utilising the communication laser's narrow beam divergence. However, as uninterrupted direct line of sight pointing is required for effective transmission, the very narrow beams pose a technical challenge due to the need for extreme pointing accuracy. The requirements for the pointing, acquisition and tracking subsystem are major limiting factors in designing laser-based communication systems (both in terrestrial and space applications).

This project focuses on developing advanced control and signals processing techniques for applications such as satellite laser-based communication that are required to quickly establish communication with different ground terminals or even other satellites in orbit.

High cost of production is one of the key bottlenecks in the cultivated meat industry. This project aims to address this problem through identifying suitable easily accessible and affordable plant-based scaffolds, recognizing scale-up parameters using an inexpensive mini bio-reactor and optimizing media feeding strategy.

Key areas within project include characterisation of the mini bioreactor process parameters, screening multiple plant scaffolds for use as inexpensive easily available scaffolds for long term culturing of fish cells; Culturing of fish cells in scaffolds for long term in custom made mini bioreactors and reducing cost of scaffolds for long term fish cell culturing.

Other areas of focus include protecting shear sensitive cells from hydrodynamic shear by employing 3D scaffolds for cell proliferation and shear protection; also, bio-process scale up through characterisation and operation of an inexpensive mini bio-reactor and achieving cost reduction through optimisation of media feed strategy.

Echocardiography (ultrasound of the heart) is the first test of choice for Cardiologists since, compared to other imaging methods, as it is less expensive, more mobile, non-invasive and radiation-free. However, the image quality is lower than other techniques like MRIs and CT scans.

US2.ai is trying to bridge the gap by developing software to bring AI-enhanced imaging resources to everyone. The project consists of developing AI tools to democratise echo, the most commonly used tool for detecting heart risk.

We are looking for the best of the best who are truly passionate about advancing our technology and want to change the world.

Venti is developing self-driving container trucks, aka Autonomous Prime Movers (APMs) to move shipping containers between cranes within the PSA Singapore port. The project aims to alleviate the strains in supply chain by addressing manpower shortages and further improving operational efficiencies. The port offers unique challenges for deployment of self-driving vehicles such as high environment change (variable stock of stacked shipping containers, moving cranes), high occlusions from large actors and structures, all weather operational demands, tight positional tolerances for interfacing with cranes, interfacing with port specific infrastructure, specialised port traffic patterns with corresponding traffic rules, and control of long articulated vehicles (truck with trailer) under variable loading up to a maximum of 65 metric tonnes. Other problems are common to urban traffic scenarios, such mixed traffic (human and robot) driver navigation of intersections and lane changes with travelling speeds of up to 40 kph, which demands APM perception range of 150m.

The project is in collaboration with PSA Corporation Singapore. The target is to deploy autonomous prime movers in the port to move the containers from one place to another within the port. The autonomous prime movers are to follow all the operation rules and conditions in the port and need to merge with manual driving prime movers at the same time. The travelling speed needs to be at 40km/h with a perception distance to reach 150m. At the same time, the autonomous prime movers need to interface with the different kinds of cranes and other port specific equipment.

Visual inertial odometry is an uprising approach for robotics localisation as an alternative to the more mature and conventional LiDAR-based localisation. Inevitably, a shortcoming of vision perception is that it has a shorter perception than LiDAR’s, making it often less reliable in vast areas. However, visual perception still appeals to a large audience of technical users because of image sequences contain huge amount of information which have many other potential uses.

This project is established for a specialised field that focuses on vision perception solutions for mobile robots to achieve autonomous navigation capabilities in GPS-denied environments.

The target and emphasis are to grow a suite of lightweight vision sensors and further develop an advanced vision perception software stack.

Hence, in this project, the team’s primary aim is to fuse object instance detection results with the vision-based odometry to achieve optimal localisation with little or no drift in commercial settings.

Robotics visual perception can generate two important types of information - geometry and semantics. The former has been well developed by several major players in the market, while the latter poses a higher technical barrier and therefore has become the focus of this job position.

This project is established for a key purpose of driving products that will democratise spatial perception of future autonomous and tele-operated robotic systems. Vilota specialises in vision perception solutions for mobile robots to achieve autonomous navigating capabilities in GPS-denied environments.

The target and emphasis are to grow a suite of vision sensors and further develop an advanced vision perception software stack.

To achieve scalable autonomous applications, we currently develop a set of algorithms to recognize navigational-related features (like road signs and markings) common obstacles (like windows, doors, trees, and other mobile robots), and to further reason about the interrelationships of the objects in an image.

In this project, candidate will build upon the existing deep-learning frameworks currently being used by the industry.

3D occupancy perception, also known as stereo depth perception, is a fundamental building block of spatial perception. The underlying principle is called triangulation, i.e., finding matched points between two adjacent overlapping images captured by a stereo camera, and calculating the disparities between the matched points to derive depth information.

This project is established for a key purpose of driving products that will democratise spatial perception of future autonomous and tele-operated robotic systems. Vilota specialises in vision perception solutions for mobile robots to achieve autonomous navigating capabilities in GPS-denied environments.

The target and emphasis is to grow a suite of vision sensors and further develop an advanced vision perception software stack.

By using state-of-the-art dense depth estimation methods, the primary aim of this project is to use vision-based perception to produce images of significantly higher resolution in comparison to competing technologies using multiple cameras.

WaveScan specialises in the R&D, manufacturing and commercialisation of radar imaging technology for non-destructive testing (NDT) applications.

System integration is key to their solution, allowing customers to quickly deploy their technology through full-scale automation for their diverse inspection needs. Some automation platforms that their scanners work with include robotic arms, robotic crawlers, stationary mounting platforms, UGVs and drones.

They are test-bedding their technology with multiple infrastructure stakeholders such as HDB, JTC, and private residential developers in Singapore. They are also scaling up overseas markets in Japan, the U.K. and Europe.

WaveScan constantly advances its technology with their expert team of scientists and engineers at WaveScan and at A*STAR.

As their technology advances, they deal with a wide array of composite and building materials that require robust signal post-processing and filtering algorithms to ensure the 3D holographic image produced has high resolution and is accurate and precise. As they aim to integrate the scanner onto platforms with large amounts of jitter such as drones, their signal processing algorithms are key to ensuring the image is clean and precise.

This project touches on three key themes - 
The first is the design of high throughput synthesis and testing capabilities to prepare and evaluate new materials. This will involve the fabrication and assembly of our tools as well as the integration onto our IoT platform by developing the control software.

The second is performing the experiments using the high throughput experimentation through a proper design of experiment, to generate high quality experimental data.

The third, will be to manage that data, including the proper visualization, curation and archival of data. The entire experimentation workflow will be driven by machine learning where new experimental conditions will be suggested by a machine learning algorithm that is trained on existing experimental data.