Open Ph.D. position on the theoretical foundations of information science
Taming Physical Inverse Problems over the Continuum
Keywords
Research scope
Inferring on statistical properties of probability distributions from their empirical samples or recovering highly structured signals from their coarse and distorted measurements are ubiquitous, yet challenging tasks in signal processing and machine learning. Those problems find myriad applications in the area of data and experimental sciences, including data classification, distribution learning, optical and radar imaging, astronomy, telecommunication, and the identification of neural recordings. From a mathematical perspective, those inference problems can often be translated in a versatile fashion as to reconstruct a continuously valued measure from low-dimensional observations.
The general research scope of the Ph.D. project is at the intersection of Data Science, Information Theory, and Theoretical Machine Learning. It will aim to investigate the statistical feasibility of specific continuously valued inverse problems and to develop numerical methods to tackle modern problems arising from physics, imaging and communication, and network modalities. A particular attention will be placed on algorithmic scalability to large data volume and robustness to low-quality inputs. Applicative areas include but are not limited to:
During the Ph.D. project, the successful applicant will be strongly encouraged to develop and strengthen his/her own research interests and to construct his/her personal research plan within this generic scope.
Requirements
The candidate is expected to hold or be close to obtaining a Master’s degree (Diplôme d’Ingénieur, M.Sc., or equivalent) and to have a strong mathematical background. Graduate-level knowledge in statistics, information theory, signal processing, or optimization would be appreciated.
The candidate must be self-motived and have professional written and oral communication skills in English.
Work environment
The successful candidate will work within the Laboratory of Signals and Systems (L2S), a friendly and convivial community of 200 researchers, students, and staff members located at CentraleSupélec, Université Paris-Saclay. The L2S (https://l2s.centralesupelec.fr) is one of the leading European laboratories with expertise in the fundamental and mathematical aspects of telecommunication, signal processing, and control systems. The Université Paris-Saclay is a top-ranked omni-disciplinary research-intensive French university.
The position will be in Gif-sur-Yvette, Paris area, France.
Upon integration, the candidate will be supervised by Maxime Ferreira Da Costa. More information on this call and his current research interests can be found at https://maximeferreira.github.io/.
A candidate’s defense and security background check, in accordance with French legislation, is part of the recruiting process.
Compensation
This Ph.D. position is fully funded (tuition fees + salary) for three years.
Timeline
The position is available immediately and will remain open until filled. Candidates are encouraged to apply early. The provisional starting date is summer/fall 2023 with a possibility to start by an internship on spring 2023
Contact/Application
Please feel free to contact maxime.ferreira@centralesupelec.fr to apply or for any additional information.
