Research Associate (DesCartes – WP3)

The DesCartes programme is developing a hybrid AI, combining Learning, Knowledge and Reasoning, which has good properties (need for less resources and data, security, robustness, fairness, respect for privacy, ethics), and demonstrated on industrial applications of the smart city (digital energy, monitoring of structures, air traffic control).

The program brings together 80 permanent researchers (half from France, half from Singapore), with the support of large industrial groups (Thales SG, EDF SG, ESI group, CETIM Matcor, ARIA etc.).

The research will take place mainly in Singapore, at the premises of CNRS@CREATE, with a competitive salary and generous funding for missions.

Read more about the DesCartes program here.

The main objective of WP3 is to support the whole Descartes program in order to develop advanced optimization-based solutions in the context of hybrid AI. Any AI system or machine learning algorithm ultimately involves a formulation with an objective or loss function to be minimized. The modelling of the problem, as well as the chosen objective function optimization algorithm, is crucial to the success of the overall AI task. This is all the more crucial in the context of hybrid AI, which seeks to integrate physics-inspired models with machine learning algorithms. We will address this problem from two complementary angles, namely optimization-based methods and machine learning-based methods.

WP3 is looking for a Research Assistant in optimization and machine learning with application to signal and image processing. The objective is to tackle inverse problems by proposing new approaches intertwining optimization and machine learning.

Minimum:

– Master’s degree in Mathematics, Computer Science, Computer Engineering or related fields.

– Background in Signal and Image processing, Optimization and Machine Learning.

– Familiarity with Python.

Desirable:

– Research experience in machine learning.

Salary Range: S$4,000-S$6,300 (depending on suitability and experience)

Workplace Address: CREATE Tower, 1 Create Way #08-01 Singapore 138602

– Interested applicants please send your resume to Caroline Chaux (Prof) – caroline.chaux@univ-amu.fr and Vincent Tan (Prof) – vtan@nus.edu.sg

– Please attach your full CV, with the names and contacts (including email addresses) of two character referees.