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biomedical research. Your profile Master's degree in computer science or related discipline Experience with Python and recent deep learning frameworks (e.g. Pytorch, MONAI) Strong interest in image analysis
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costs and energy requirements of state-of-the-art deep learning models significantly, while democratizing them for a vast community of users, researchers, and practitioners. The task is to perform just
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learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing and implementing NLP pipelines for clinical text processing, semantic annotation
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candidate will hold a PhD in geosciences, applied machine learning, data assimilation, or applied mathematics. The selection will be based on the following scientific and technical criteria: Experience in
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techniques for biological tissue studies. The candidate will work is this research team. The PhD candidate will present his work in international conferences around the world (1 to 3 international trips
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the field of frugal or green AI TECHNICAL SPHERE You have a proven experience in frugal, green or low-resource AI Strong grasp of deep learning architectures (CNN, RNN, Transformers, LLMs). Experience in fine
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perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon these pNTA results
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PhD Positions Application Deadline 3 Mar 2026 - 20:00 (Europe/Paris) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 1 Apr 2026 Is the job funded through the EU
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PhD students and 40 fixed-term contract staff. The research work will be carried out within the D2-FTC department, Fluids, Thermal and Combustion, which corresponds to a continuum ranging from fluid
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the University of Poitiers and ISAE-ENSMA (partners). It has around 600 staff: 200 EC, 40 researchers, 100 IT, 180 PhD students and 40 fixed-term contract staff. The research work will be carried out