Research Fellow in Optimization-driven hybrid AI (DesCartes – WP3, WP8, WP9)
Job offer posted on 11 August 2022
DesCartes Program is looking for 1 year position in Optimization-driven hybrid AI
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.
WP3 aims at
supporting 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.
WP8 aims at
producing general methods on decision making frameworks based on Hybrid AI. Topics of interest cover smart sensing, understood in the sense of optimal generation of data from sensors, as well as interactions between data and physics-based models.
Fokker-Planck equations arise in several forms in forward and inverse problems. Its numerical solution is particularly challenging for classical numerical solvers when the equation has nonlinearities and it is posed in a high-dimensional space. The postdoctoral candidate will engage in a collaborative effort to develop novel methods based in viewing the Fokker-Planck solution as a moment problem optimization method (based on sums of squares – SOS). This approach has potential to address the above bottlenecks since polynomial nonlinearities are automatically treated as unknown moments of the solution, and there is room to incorporate low rank feature in the solution search in order to fight against the curse of dimensionality.
EXPERIENCE & QUALIFICATIONS
Competences in some of the domains listed below will be highly considered:
- Computer Science
- Partial differential equations
- Machine Learning.
- Fokker-Planck equation
- Optimization (convex, non convex)
- Machine learning
- Deep learning
- Programming (python or Julia or Matlab)
FURTHER INFORMATION & CONTACT
Salary range: 55K to 85K SGD (depending on suitability and experience)
Workplace address: CREATE Campus, CREATE Tower, 1 Create Way #08-01 Singapore 138602
Interested applicants please send your resume to:
Email : email@example.com
Email : firstname.lastname@example.org
Email : email@example.com
Email : firstname.lastname@example.org
Email : email@example.com
– Please attach your full CV, with the names and contacts (including email addresses) of two character referees.