The School of Physical and Mathematical Sciences (SPMS) at NTU Singapore hosts research and education activities in two divisions: Division of Mathematical Sciences (MAS) and Division of Physics and Applied Physics (PAP). MAS covers diverse topics ranging from pure mathematics to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and photonics. Over the years, SPMS has attracted talented individuals from around the world and Singapore to join as scientific leaders and researchers.

Join Assistant Professor Jeremie Houssineau (SPMS) and Professor Yew Soon Ong (CCDS) as a Research Assistant to contribute to a project focused on modelling and quantifying uncertainty for foundation models. You will be part of a larger project focused on developing theoretical foundations for deep foundation models, in collaboration with Professor Taiji Suzuki from the University of Tokyo and Associate Professor Atsushi Nitanda from A*STAR.

Key Responsibilities:

Research & Development

• Conduct original research in deep learning, related to uncertainty quantification for large models.
• Explore cutting-edge advancements in AI and relate it to the main research objective.

Experimentation & Implementation

• Assist the team in developing and implementing related algorithms, models, and techniques.
• Perform rigorous benchmarking and evaluation of the developed models.
• Optimize algorithms for efficiency, scalability, and robustness.

Collaboration

• Work closely with the PI and the project team.
• Collaborate with PhD students, undergraduate researchers, and postdocs.

Writing & Publications

• Write technical reports and research documentation.

Job Requirements:

Educational Qualifications

• BSc, or equivalent, in Statistics, Computer Science, Applied Mathematics, or a related field.
• Some research background in Statistics or Machine Learning.
• Entry level candidates are welcome to apply

Technical Competencies

• Mathematical & Statistical Foundations: Strong understanding of statistics, probability, optimization, and linear algebra.
• Machine Learning: Deep learning, probabilistic modeling, generative models, etc.
• Programming & Software Development: Proficiency in Python, PyTorch, JAX, or other ML frameworks
• Computing: Some experience with large-scale datasets, parallel computing, and GPUs/TPUs.
• Algorithm Development: Ability to develop and optimize Machine Learning algorithms for various applications.

Research & Analytical Skills

• Ability to design and execute experiments for evaluating ML models.
• Critical thinking and problem-solving abilities.

Soft Skills

• Communication: Ability to present research findings effectively through writing and presentations.
• Collaboration: Experience working in interdisciplinary teams with researchers from diverse backgrounds.
• Project Management: Ability to meet deadlines.
• Adaptability & Innovation: Willingness to explore new methodologies.

We regret to inform that only shortlisted candidates will be notified.