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develop machine learning approaches (deep learning) to understand the eco-evolutionary mechanisms underlying biological diversity from environmental (phylo)genomic data. - Methodological developments in
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), whose objective is to extend the HLA-Epicheck model, originally developed within the framework of a PhD thesis, and to implement new deep learning approaches to assess donor–recipient compatibility in
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nervous system to modulate future aversive/nociceptive experiences (Merabet et al., in preparation). Notably, this effect participates in how flies learn the relative aversive value between aversive
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instruments and high throughput genomics that informs advanced numerical analysis methods (modeling, statistics, machine learning). Plankton encompasses all organisms roaming with marine currents. Those
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” focusing on the effect of a fluctuating environment on the collective dynamics of self-propelled agents, a numerical part on “reinforcement learning” focusing on optimizing communication between agents in a
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Eligibility criteria PhD in Materials Science, Process Engineering, or a similar field. Required Skills: Proficiency in ceramic and composite material development processes, as well as structural, mechanical
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collaborate with global leaders (e.g., Mediterranean Institute of Oceanography, Southern Denmark University). - Publish high-impact research as first author, present at international conferences, mentor
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battery cathode materials within the ULTRABAT project. - Modeling the interface and local NMR parameters in collaboration with the modeling group at DTU (Denmark) and IMN (Nantes, France). - Conducting NMR
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» AlgorithmsYears of Research ExperienceNone Additional Information Eligibility criteria - PhD in one of the following areas (or related fields): * Machine learning / deep learning * Quantum computing / quantum
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. • Strong knowledge of signal processing methods and machine learning. • Familiarity with regulatory and ethical constraints in research involving sensitive data. • Ability to work closely with