Research Fellow (Statistical Machine Learning)

Join Our Team at the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore

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 Fellow 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

- Develop and implement the related algorithms, models, and techniques.

- Perform rigorous benchmarking and evaluation of the developed models.

- Optimize algorithms for efficiency, scalability, and robustness.

• Collaboration & Mentorship

- Work closely with the PI and the project team.

- Collaborate with PhD students, undergraduate researchers, and other postdocs.

- Provide mentorship and guidance to junior researchers.

• Writing & Publications

- Prepare and submit research papers to peer-reviewed conferences and journals.

- Write technical reports and research documentation.

- Present research findings at conferences.

Job Requirements:

• Educational Qualifications

- Ph.D. in Statistics, Computer Science, Applied Mathematics, or a related field.

- Strong research background in Statistics and Machine Learning.

- Proven ability to conduct independent research with a strong publication record.

- 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: 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.

- Experience in writing and reviewing academic papers.

- 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 manage research projects, meet deadlines, and mentor junior researchers.

- Adaptability & Innovation: Willingness to explore new methodologies.

The College of Science seeks a diverse and inclusive workforce and is committed to equality of opportunity. We welcome applications from all and recruit on the basis of merit, regardless of age, race, gender, religion, marital status and family responsibilities, or disability.

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