The Summation Programme is beyond a typical tech apprenticeship in Singapore, it will enable you to gain invaluable feedback and mentorship while providing you with an opportunity to hone and test your skills — to be better at your game. Specialised training workshops will also be made available to you, along with exclusive invites to a multitude of Deep Tech industry and community events.
programme* for fresh graduates.
Apprenticeship* for students.
S$3,000 — S$6,000*
NTU Undergraduate, School of Physical and Mathematical Sciences
NUS Graduate, School of Computing
Earn as you learn. Receive SGD3,000 — SGD6,000* as you work on impactful projects.
Work on exciting Deep Tech projects utilising technologies such as Artificial Intelligence, Machine Learning, Deep Learning, and Blockchain across various industries.
Experience with Mentorship
A great opportunity to hone and test your skills, under the guidance of a highly skilled technical mentor.
Gain new insights and skills through specialised workshops in your project area.
Get plugged into the Deep Tech ecosystem through exclusive invites to various Deep Tech industry and community events.
Make new friends and share experiences and learnings with a highly engaged and vibrant community of Summation Alumni through frequent gatherings.
Full-time students and fresh graduates, register your interest for our next batch here.
You should be an undergraduate, Master’s or PhD student with universities, or a fresh graduate with < 1-year of working experience.
Programming skills are required, and basic Deep Tech knowledge will be a plus point.
You should have experience from tech projects, other internships and relevant competitions.
Please see the FAQs for more details and eligibility criteria.FOR LOCAL TALENT FOR INTERNATIONAL TALENT
Foreign students based outside of Singapore are welcome to apply. But in light of current travel restrictions, actual matching
to opportunities will only take place once the restrictions have been lifted. For more information, please refer to our FAQs.
AFF1: Social Media Inference Through Deep Learning
At Affable, we profile millions of users every day and use Machine Learning to infer things such as their interests, age, and location. We use a host of signals to make such inferences, including but not limited to their profile pictures, posts, stories, and captions.
This project involves conceptualising, training and deploying Machine Learning models that can infer even more characteristics about social media users. Some of the traits we want to extract further are psychographics, education level, job function, and income level.
The main challenges of this project are two-fold: volume and speed. We are tracking over 1.5 billion social media posts and 300 million social media users. Machine Learning at this scale is challenging, time-consuming but also very rewarding. The apprentice will get first-hand experience in building models that are not only accurate but also fast and production-grade (some of our models run inference on over 20 million images per day).
KON1: Predictive Analytics Using Machine Learning for Businesses
The purpose of the project is to build a fast and reliable time-series forecasting pipeline for sales data. This project will give the apprentice an opportunity to work on an essential financial dataset, and evaluate internal and external factors affecting sales. This project will require the apprentice to assess various forecasting engines to identify the right model based on the data for our customers. The main aim of the product will be to provide our customers the most accurate forecast with actionable insights.
KON2: Information Extraction Using Natural Language Processing
We are developing an automated system which requires zero configuration or any upfront annotation using the latest developments in the fields of CV and NLP for information extraction. We are building a next generation human interaction system which will be as natural as asking questions to an in-house analyst who knows all the answers. We are doing this by using state-of-the-art DeepLearning research in NLP to understand users’ intention and entity from the natural language to give the most appropriate answer.
ATO1: Data Modelling for Gravity-Based Resource Exploration
At Atomionics, we are building sensing technology which performs 1000x better than the current state-of-the-art technology using Atom Interferometry, harnessing the potential of wave-nature of atoms. The current ongoing project is building a portable single-axis atom interferometer which can measure gravity very precisely and help in accurately pinpointing hydrocarbon and mineral reserves helping ease the energy needs of the world. The apprentice will build data models to interpret this new gravity data and derive insights.
ATO2: Atom Interferometry for Gravity Measurements
The current ongoing project is building a portable single-axis atom interferometer which can measure gravity very precisely and help in accurately pinpointing hydrocarbon and mineral reserves helping ease the energy needs of the world. The apprentice will be working closely with the product development team, an interdisciplinary mix with prior experience in Atomic physics, biomimetic underwater robots, high altitude pseudo satellites and balloon satellites, and advised by leaders in cold atom physics and navigation systems.
MOV1: Computer Vision for Robotics Localisation and Navigation
As a Robotics Engineer in Movel AI, you work with the world's best robotics scientists and engineers to implement mobile robotics solutions for some of Singapore's key industry sectors. You get to experience the early life of a Deep Tech startup and sharpen your software engineering skills by working on real-life and critical robotics systems.You will get a chance to work on the full stack robotics software system and hands-on experience with some of the world's most advanced robots. The challenges you can tackle includes:
- Implement and optimise computer vision algorithms for robot localisation
- Design and implement computer vision and deep learning algorithms for robot navigation
- Technical Proposal Generation based on client needs
MED1: Deep Learning on Retinal Images
We have gathered a database of about half a million retinal images and are continuously working with leading ophthalmologists.In this project, the apprentice will work on a screening solution starting from literature review to customer deployment. He/she will design a Deep Learning-based algorithm to detect a specific eye condition, working closely with the VP of AI as well as medical consultants.
EKO1: Deep Learning on Echocardiograms
We are building out a global data network housed in some of the world's preeminent imaging labs and academic institutions. This data serves as the foundation for Machine Learning and statistical analysis for disease prediction studies. We are collaborating with some of the world’s top cardiovascular researchers to structure, analyse and publish this data while incorporating the algorithms into a SaaS-based product for clinical use.
You will be required to support and participate in Machine Learning trials, and to organise and analyse large volumes of echocardiograms. There will be opportunities to gain experience with a variety of Deep Learning classifications and segmentation techniques in collaboration with some of the world's premier cardiology centres.