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, seeks to recruit a junior research scientist to develop AI-enabled healthcare applications. Key Responsibilities: Develop and fine-tune computer-vision models, instance segmentation, and retrieval-based
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research units: GEPEA, IRISA, LATIM, LABSTICC, LS2N and SUBATECH. The proposed thesis is part of the research activities of the team MOTEL (Models and Tools for Enhanced Learning) of the Lab-STICC laboratory
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will produce a dataset on social interactions aimed at training machine learning models for human-robot interactions. Robot decision using internal simulations : As mentioned at the beginning of
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, ML models, and data processes This role requires advanced data science and machine learning expertise, proficiency with Python ML libraries, strong SQL programming skills, experience with data pipeline
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/deploying deep learning models and machine learning applications. Computer skills: Python (PyTorch, TensorFlow), databases (MySQL), 3D Slicer, ITK-SNAP, OpenCarp. Previous experience in research activity in
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. The successful applicant will develop a predictive pipeline using atomistic modeling and machine learning to identify optimal "seeds" for directing crystal growth, followed by rigorous experimental testing
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Federated learning (FL) is an emerging machine learning paradium to enable distributed clients (e.g., mobile devices) to jointly train a machine learning model without pooling their raw data into a
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demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands‑on experience with common machine learning / deep learning
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Deployment Strategies - Model Compression: Investigate techniques such as quantization, pruning, and knowledge distillation to reduce the computational and memory footprint of deep learning models without
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support needed to execute machine learning (ML) aims, handling large, complex datasets that exceed the capacity of general staff. By integrating daily data cleaning with advanced modeling, this position