Multimodal AI algorithm for predicting the locomotor behavior of aquatic animals in turbulent flows

Organisation/Company: Laboratoire XLIM Research Field Computer science » Digital systems Computer science » Informatics Engineering Researcher Profile Recognised Researcher (R2) Leading Researcher (R4) First Stage Researcher (R1) Established Researcher (R3) Application Deadline 16 Apr 2026 - 22:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Oct 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The objective of this thesis is to develop a deep neural network model capable of predicting the behavior of aquatic animals in novel environments. The model will be trained on a hybrid corpus combining video data and fluid flow data derived from numerical simulations and experimental measurements.

Proposed Methodology:

• Automated Video Analysis: Detection and segmentation of animals to extract their locomotor and physiological characteristics ;
• Data Fusion: Alignment of biological parameters with the physical characteristics of the flow ;
• Multimodal Training: Design of an AI model capable of simultaneously interpreting heterogeneous data sources ;
• Prediction: Generation of trajectories in new habitats or hydrological contexts ;
• Evaluation: Comparison between predicted trajectories and observed data.

Scientific Challenges:

Collaborative work between XLIM and MIA (Rabu2026) has demonstrated the superiority of Deep Learning approaches in processing complex videos characterized by turbulence and visual artifacts. Two major challenges have been identified:

• Extraction of physiological characteristics: Small-amplitude movements (gills, fins, antennae) are difficult to capture. This thesis plans to use motion magnification methods combined with Physics-Informed Neural Networks (PINNs). These networks allow for the coupling of visual data with mathematical swimming models to accurately estimate their parameters ;
• Multimodal AI Architecture: Proposing an architecture capable of effectively merging video, experimental, and simulated data to ensure reliable prediction of aquatic animal behavior across different experimental or simulated configurations.