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several major types of mobility modeling, with the aim of improving their respective efficiency and usage: four-step models, multi-agent systems and a mobility model developed in the PhD thesis of Louisette
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FieldMathematicsYears of Research ExperienceNone Additional Information Eligibility criteria PhD in computer science, deep learning, or data science. Experience with multimodal models for biological data. Website
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the time of their application, a PhD degree in geology (including (bio)geochemistry, mineralogy/crystallography or experimental/isotope geochemistry/petrology), chemistry, physics or materials sciences. We
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also interdisciplinary knowledge on the subject. More precisely: PhD degree in computer science, machine learning, computational biology, or a closely related field Strong research track record
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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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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
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: Marine Biodiversity and ecosystem functioning across spatial, temporal, and human scales”. The overall aim of the project is to acquire knowledge of the principles governing the structure, dynamics
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Information Eligibility criteria Applicants should hold a PhD in theoretical chemistry, physics, materials science, or a related field; -demonstrate strong expertise in machine learning (regression, neural
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Candidate Profile Training and Skills required (Recent) PhD in bioinformatics, statistics, or computer science with knowledge and interest in biology Track record of creativity in developing analytic