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the knowledge acquired during the PhD with team members and acquire new knowledge. - Engage with the Local team at LIPN and the wider national community working on proof theory, programming languages and
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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neurological departments of Lyon's hospitals. Lyon is a vibrant and beautiful city, just 1 hour from the French Alps by car, and 2 hours from the Mediterranean coast and from Paris by train. More on our work can
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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for future career development in both academia and tech. Profile Required: PhD in ML, computational neuroscience, physics, engineering, or related field Strong experience in machine learning (PyTorch
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of optimization and machine learning. • Knowledge of reinforcement learning and black box optimization would be a plus. Skills • The candidate must be comfortable with algorithmic development using
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new insights into the phenomena observed and enrich the databases required for deep learning methods. The neural networks currently being developed at LISTIC to detect and segment areas of movement in
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. In particular, he/she will be expected to :• Select and evaluate the most suitable approaches from the wide range of machine learning and computer vision methods available in the literature, with
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a machine learning model (foundational model) to propose protocols of sequential induction of transcription factors to generate desired cell subtypes. The project will be conducted in close