Research Associate (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) as a Research Associate to contribute to a project focused on robust Bayesian inference with possibility theory. Robust inference is crucial for many real applications in which datasets are invariably corrupted by outliers and where the posited models are only coarse approximations of the real data generating mechanism. Possibility theory offers new insights into robust inference by directly quantifying the agreement between data and model, hence making easier the detection of inconsistencies between the two.

Key Responsibilities:

• Research & Development

- Conduct original research in robust Bayesian inference, for both batch and online problems.

- Explore cutting-edge advancements in robust inference 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 and scalability.

• Collaboration & Mentorship

- Work closely with the PI and the project team.

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

• 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

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

- Research background in Statistics or Machine Learning.

- Proven ability to conduct independent research.

- Entry level candidates are welcome to apply

• Technical Competencies

- Mathematical & Statistical Foundations: Strong understanding of statistics, probability, optimization, linear algebra, and probabilistic modeling.

- Programming & Software Development: Proficiency in Python

- Computing: Experience with real datasets and parallel computing

- Algorithm Development: Ability to develop and optimize Statistical algorithms for various applications.

• Research & Analytical Skills

- Ability to design and execute experiments for evaluating Statistical 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 manage research projects, and meet deadlines.

- 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.