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ExperienceNone Additional Information Eligibility criteria The candidate's profile must match the description of the research project. The candidate must: - Hold a PhD in physics. - Have experience in plasma
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Additional Information Eligibility criteria You hold a PhD in physics, physical chemistry, materials science, or a related field. You have solid hands-on experience in material synthesis under inert atmosphere
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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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Postdoctoral researcher in the analysis of single-cell and spatial transcriptomics experiments (M/F)
engineeringYears of Research Experience1 - 4 Additional Information Eligibility criteria - Holder of a PhD in a field related to cancer and treatment resistance - Conducting and analyzing single-cell and spatial
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Additional Information Eligibility criteria • PhD in developmental biology, or related fields such as regenerative biology, cell biology, molecular genetics • Experience in microscopy (preferably live
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Additional Information Eligibility criteria ● A relevant PhD degree in semiconductor physics, material sciences, or similar. ● Hands-on experience with III-V semiconductor deposition processes (e.g. MOCVD, MBE
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LanguagesFRENCHLevelBasic Research FieldChemistryYears of Research ExperienceNone Research FieldPhysicsYears of Research ExperienceNone Additional Information Eligibility criteria Holder of PhD in chemistry, the candidate
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Eligibility criteria PhD in Mathematics. Candidates with expertise in one of the following research areas are encouraged to apply: - Complex foliations; - Birational geometry; - Qualitative theory of real
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Additional Information Eligibility criteria The candidate should have a PhD in Biology/Evolutionary Biology and skills in bioinformatics and macroevolution (ideally models of phenotypic evolution
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scienceEducation LevelPhD or equivalent Skills/Qualifications PhD in computer science Background in probability, Markov chains, MDPs Knowledge about reinforcement learning and planning are a plus but not necessary