Research Assistant (Statistical Machine Learning) Full-time

Research Assistant Position

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.

Hiring Institution: NTU