Faculty position
posted on April 19, 2024

Tenure-track position (CPJ): Machine-learning for the data-driven modeling of complex dynamical systems

Application deadline: May 15th, 2024
Start date: Sept. 2024

SUPMICROTECH (École Nationale Supérieure de Mécanique et des Microtechniques) and the Dept of Automation and Robotics, FEMTO-ST Institute in Besançon, France are advertising an exciting new tenure-track faculty position (chaire de professeur junior, CPJ) for immediate recruitment on the topic of machine learning for the data-driven modeling of complex dynamical systems, with possible application (but not limited) to the scope of the Neuro research group.

The position is funded for an initial period of 3-6 years (depending on experience), after which the person will be examined for direct promotion at the rank of Full Professor (Professeur d’Université). It includes a 200-300k€ research startup package and a reduced teaching load (from the normal 192 hrs to 64hrs yearly).

Applications should be made solely via the Galaxie portal at : https://www.galaxie.enseignementsup-recherche.gouv.fr/ensup/cand_CPJ.htm.

Research profile

Recent advances in artificial intelligence (AI) are set to radically change the fields of engineering. These new methodologies provide models capable of solving characterization and decision-making problems in fields as varied as robotics, automation, mechanics and healthcare. However, these models, often used as “black boxes”, provide no explanation of the relationships they learn to exploit between the inputs and outputs of the systems modeled, especially when these are highly non-linear and complex. On the one hand, this limits the new physical knowledge that they can bring to bear on these systems (for the purposes of modeling for control or prediction) and, secondly, the confidence that a user, whether industrial or clinical, can place in their decisions.

The aim of this faculty position is to set up a research program around the theme of explainable and physically-informed AI for modeling dynamic systems, and to steer a strong and original vision for the place of AI in the SUPMICROTECH curriculum. This research program aims to develop the next generation of machine-learning tools for controlling complex physical or physiological systems, by using a data-driven approach to discover physically interpretable models of dynamic systems based on temporal data.

For example, the project could fit in with the recent emergence of data-driven methods for identifying systems, such as reverse-correlation, from the automatic control and biological systems modeling communities (Daube et al. Patterns, 2021), symbolic regression methods (SINDY; Brunton et al. PNAS 2016) or the learning of physically constrained representations (DMD; Schmid Annual Review of Fluid Mechanics, 2022). This work could be applied to the modeling of biological biological systems, to provide new diagnostic or prognostic tools in e.g. neuroscience (Durstewitz, Koppe & Thurm, Nature Reviews Neuroscience 2023), cancerology, or new micromanipulation techniques for cellular cell characterization or surgery.

The recruited faculty is expected to join the Dept of Automation and Robotics (Automatique et Systèmes Micromécatroniques, AS2M) of the FEMTO-ST Institute, of which SUPMICROTECH is one of the host operating institutions. Research conducted within the AS2M department is based on a multi-disciplinary foundation combining mechatronics, automation and data science. Machine learning for data-driven estimation of dynamic systems is a strongly emerging field in machine-learning (see, for example, the recent AI Institute in Dynamic Systems at the University of Washington in Seattle, the L4DC cycle of international conferences, or the Deep Learning for Physical Processes research chair at Sorbonne Université). This research area is central to the FEMTO-ST Institute’s recent work, whether in the control of innovative robotic architectures robotic architectures (e.g. flexible and dexterous robotics), modeling unconventional physical systems at micro/nanometric scales (e.g. machine-cell interaction) or the prediction of complex dynamic systems in the fields of environment and health (e.g. identifying dynamical systems from neurobiological data). By bridging the gap between data science and dynamical systems theory, the project will be able to give rise to new cross-disciplinary projects combining, for example, modeling and control control or control and prediction. Finally, the project will reinforce the department’s strong recent momentum in the field of micro/healthcare technologies, with applications at the level of the cell (cancer cell characterization), organ (sensors for oncology diagnostics), or organism (neurosciences). (neuroscience). If concerned with neuroscience, the research project may benefit in particular from the Dept’s human EEG experimental platform (https://neuro-team-femto.github.io).

Teaching profile

The teaching project is part of the “artificial intelligence for engineers” theme, and aims to the strategic overhaul of SUPMICROTECH’s teaching curriculum in digital/data-science and AI for industry.

The recruited person will be part of the SUPMICROTECH computer science teaching department and contribute to training the school’s engineering curriculum by strengthening the link between training and research. They will develop AI teaching focused on Explainability (xAI) and Physically Informed AI (PINN), in interaction with the micro-mechatronic, microrobotic and biological systems or biological systems studied at the School, with a focus on the physical meaning of the models and/or their use in control or prediction frameworks.

About the environment

The recruited person will be hired by the SUPMICROTECH School of Engineering, for teaching in the School’s accredited engineering program, and will conduct their research in the FEMTO-ST Institute, all in Besançon, France.

EnsmmSUPMICROTECH-ENSMM, one of the founding members of the Université Bourgogne-Franche-Comté (UBFC), is a public higher-education institution training accredited multi-disciplinary engineers in mechanical systems, mechatronics and microsystems. Authorized by the French Ministry of Education, Higher Education and Research, the SUPMICROTECH diploma has been recognized by the Commission des Titres d’Ingénieur since 1934. SUPMICROTECH recruits mainly at Bac + 2 level and, after 3 years, awards three engineering diplomas leading to a Master’s degree. Graduates of the school are versatile engineers, able to work in R&D, design, production or marketing.

Femto With more than 750 researchers, the FEMTO-ST Institute (CNRS/Université de Bourgogne Franche-Comté) is the region’s largest engineering CNRS lab, with expertise covering all fields of system science. FEMTO’s Department of Automation and Robotics hosts about 80 researchers active in the fields of robotics, mechatronics, automatic control and artificial intelligence.


Besancon A world-heritage UNESCO site close to the French-Swiss mountains of Jura, Besançon is a vibrant, mid-size regional capital city regularly ranking best-in-France for its quality of life and surface green spaces per inhabitant, but also boasts a newly-federated university (Université de Bourgogne Franche-Comté) of more than 50k students.


Applications

The position being funded at a national level, applications should be submitted solely via the Galaxie portal at : https://www.galaxie.enseignementsup-recherche.gouv.fr/ensup/cand_CPJ.htm. Please note the Galaxie portal is mostly in French and, depending on your language level, filling in the information and various required files may be error-prone. Please plan accordingly, and seek advice from the contact persons below.

Job reference: 4094
Job description: https://www.galaxie.enseignementsup-recherche.gouv.fr/ensup/ListesPostesPublies/FIDIS/0250082D/FOPC_0250082D_4094.pdf
Deadline: 15th May 2024.

A pre-selection of applications will be made by the search committee. Successful candidates will be invited for an interview, which will include a presentation of their research and teaching project, as well as a mock teaching session, the details of which will be specified in the invitation.

Before submitting their application, we strongly encourage applicants to make contact with the FEMTO AS2M and SUPMICROTECH research teams in order to adapt their research and teaching statements to the strategic directions of the two institutions:

Prof. Yann Le Gorrec, Department Chair, Dept. of Automation and Robotics, FEMTO-ST Institute. yann.le.gorrec@ens2m.fr

Dr Jean-Julien Aucouturier, PI Neuro group, Dept. of Automation and Robotics, FEMTO-ST Institute. aucouturier@gmail.com

Prof. Christophe Varnier, Head of Studies, SUPMICROTECH/ENSMM. christophe.varnier@ens2m.fr