PhD Position F/M PhD thesis subject on Inferring Continuous-Time Reaction Models from Time-Series Data

Inria, the French national research institute for the digital sciences; France; Remote; EUR 20,000 - 26,000; Full time

Benefits offered by this job

Partial reimbursement of public travel

7 weeks annual leave + RTT days

Teleworking available

Equipment provided

Social, cultural and sports events

Job summary

Inria, the French national research institute for the digital sciences, propose une thèse de doctorat co-supervisée par François Fages et Sylvain Soliman, dans le cadre du projet INCRE. Vous intégrerez Lifeware et collaborerez avec l’UN Rostock (Allemagne) et le réseau ANR-DFG.

Les missions incluent l’étude de l’apprentissage de modèles à partir de données temporelles, le développement d’un prototype Reactmine-2 basé sur beam search, et la conception de benchmarks.

Qualifications

• Candidature à une thèse de doctorat en informatique.
• Connaissances en apprentissage automatique et données temporelles.
• Capacités en programmation et raisonnement mathématique.
• Bonne aptitude à travailler en équipe internationale.

Responsibilities

• Effectuer des recherches sur l’apprentissage de modèles à partir de données temporelles.
• Développer un prototype logiciel Reactmine-2 basé sur beam search.
• Concevoir des benchmarks et évaluer Reactmine-2.
• Publier des résultats dans des articles scientifiques.
• Collaborer avec des partenaires ANR-DFG et Rostock.

Skills

Français

Anglais

Education

Doctorat en informatique (en cours)

Job description

Inria, the French national research institute for the digital sciences

Organisation/Company Inria, the French national research institute for the digital sciences Research Field Computer science Mathematics Neurosciences Researcher Profile First Stage Researcher (R1) Application Deadline 30 Oct 2026 - 00:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Hours Per Week 38.5 Offer Starting Date 1 Dec 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number 2026-10631 Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Within the framework of a partnership (you can choose between)

• public with {French National Research Agency (ANR), and German agency DFG project INCREl

a package/model/prototype/application
more specifically dedicated to CRN model inference (Inria LIfeware) and benchmarking (Rostock)

Is regular travel foreseen for this post ?Travel to Rostock Germany once a year

Assignments :
PhD thesis co-supervised by François Fages and Sylvain Soliman

Collaboration :
The recruited person will be member of Lifeware and follow-up project team, withcollaboration with Prof Adelinde Uhrmacher, UNiv. Rostock, Germany.

Responsibilities :
The person recruited is responsible forsome subtasks of the ANR-DFG project INCRE.

Main activities (5 maximum) :

• state of the art on model learning from temporal data
• development of Reactmine-2prototype software based on beam search
• design of synthetic benchmarks and comparative evaluation of Reactmine-2
• model inference in presence of unobserved variables and evaluation on real biological data
• publicationa and communications in scientific journals and conferences

Additional activities (3 maximum) :

• participation to vulgarisation events and/or publications
• Essential qualities in order to fulfil this assignment are feeling at ease in an environment of scientific dynamics and wanting to learn and listen.
• Mathematical rigor
• At ease in programming

Languages FRENCH Level Basic

Languages ENGLISH Level Good

Additional Information

• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities