Fixed-term researcher – Quantum optimization (QUBO) for marine fish trajectory reconstruction on Pasqal's Ruby quantum computer (TGCC) (M/F)

Job Description

Missions

The position is part of the Q-FINS project (Quantum FINe-scale route inference for marine Species), carried out at the Laboratory for Ocean Physics and Satellite remote sensing (LOPS, UMR 6523 UBO/CNRS/Ifremer/IRD, Brest) under the supervision of Jean-Marc Delouis and Tina Odaka.
Q-FINS aims to reconstruct the trajectories of marine fish from biologging data (depth and temperature recorded by electronic tags). This inverse problem becomes strongly combinatorial once global biological constraints (energy budget, behavioural switches) are introduced, which motivates its formulation as a quadratic unconstrained binary optimization (QUBO) problem suited to quantum computing.
The mission is to develop and test these methods on Pasqal's Ruby neutral-atom quantum computer hosted at TGCC, and to benchmark them against classical approaches.

Activity

– Implement the explicit position-by-time QUBO formulation described in the Q-FINS technical note: observation terms (temperature/depth), movement terms, endpoint constraints (release and recapture) and at least one non-local biological constraint (e.g. metabolic budget).
– Map these QUBOs onto the Ruby neutral-atom quantum computer using Pasqal's software tools (e.g. Pulser) and run them at TGCC on reduced instances (about 30 to 40 qubits, 10 to 15 time steps, synthetic or undersampled tracks).
– Benchmark the results against classical baselines on the same objective: dynamic programming / HMM, branch-and-bound, simulated annealing.
– Assess performance: best energy, time-to-solution, reproducibility, trajectory plausibility, number of binary variables.
– If time allows, explore the compressed move-based encoding.
– Document the code and write a summary report of the results.
No staff supervision.

Your Profil

Skills

PhD required in physics, applied mathematics, computer science or a related field. Experience in combinatorial optimization and QUBO/Ising formulations; knowledge of quantum computing, ideally analog neutral-atom platforms. Strong Python skills and experience with high-performance computing environments (Linux, job schedulers). Knowledge of inverse problems, data assimilation or movement ecology is a plus.
Autonomy, rigour, ability to deliver results quickly within a short assignment; scientific English.

Your Work Environment

The recruited researcher will be hosted at LOPS (Brest) and will work closely with Jean-Marc Delouis and Tina Odaka, who lead the Q-FINS project.
Objective: deliver within two months a first proof of concept of trajectory reconstruction by quantum optimization on Ruby, supported by a quantitative comparison with classical methods.
Resources: remote access to Pasqal's Ruby quantum computer at TGCC, HPC resources for the classical baselines, the Q-FINS technical note, biologging data and environmental fields provided by the team. The contract is funded by IAOCEA.

Constraints and risks

Short assignment (2 months) with tight objectives. Work on a remote quantum computer: dependence on Ruby access slots and TGCC queues. No on-call duty.

Compensation and benefits

Compensation

€4,258.87 gross per month

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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