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Field
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market-related violences in Marseille and its surroundings (DRAME project funded by MILDECA - France's interministerial strategy for mobilization against addictive behaviors), coordinated by Marwan
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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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of 3D crystalline structures; – depending on the candidate's profile, implementing machine learning methods (AI & machine learning) for the analysis of physicochemical data from the hpmat.org database
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(particularly Deep Learning), will also make it possible to leverage the collected data to enrich knowledge of ovine behavior. The candidate will join a dynamic research group within the Image/Vision team
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in problem-solving and independent thinking; Experience in brain stimulation would be an asset or willingness to learn; Proficiency in English (speaking French would be a plus). This position offers
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or machine learning applied to brain signals would be an advantage. We are seeking a highly motivated, rigorous and inquisitive researcher, ready to commit to a project at the interface between basic and
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structure calculations, vibronic property simulations, and analyzing surface adsorption phenomena. Knowledge of machine learning potentials (e.g., GAP, ACE) or reactive force fields is a plus, as fallback
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support machine learning applications for analyzing electron microscopy images of nanoalloys. Model interactions between nanoalloys and carbon substrates to reflect experimental conditions, incorporating
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their effectiveness remains limited by the inherent constraints of fuzzing techniques. As an alternative, we propose exploring reinforcement learning (RL) as a promising approach for vulnerability assessment in SoCs
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e.g., ultra-cold gases of bosonic or fermionic atoms, machine learning technologies and quantum computing. At the same time, we work in close connection with IJCLab experimentalists, particularly