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machine learning tools. The postdoctoral fellow will contribute to various aspects of the project, such as: * developing new theoretical and numerical approaches for determining the thermodynamic and
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physics groups, as well as the DELPH (Detection and Lasers for Physics) and GTA (Acquisition Techniques Group) groups. The Physics Division is composed of 25 permanent physicists, around 20 PhD students and
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Complexity; Quantum Information and Quantum Learning. The successful candidate will work closely with Augustin Vanrietvelde and his team on the topics of quantum foundations, quantum reference frames and
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the synthesis of the nanowires and their assembly into networks (by soft chemistry), while the synthesis of the matrix (CVD deposition) will mainly be carried out by a PhD student already recruited
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facilities. Skills/Qualifications PhD in microbiology, molecular biology, or related field Strong experience in bacteriology and molecular mechanisms of pathogenesis Previous experience with insect models and
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for integration into diagnostic tools Preparing manuscripts for submission to both medical and machine learning journals Qualifications Candidate profile: Required: PhD in mathematics, statistics, data science
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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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copolymers, which will then be evaluated for their degradability and mechanical properties. Using active learning, a branch of AI, the research will be guided through the large parameter space of copolymers
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Desired level of education : PhD in physics or related fields Experience required : PhD and/or postdoc experience in one or more of the following areas: theoretical quantum optics, theoretical atomic and
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) and Aurélien Quiquet from the Laboratoire des Sciences du Climat et Environnement (LSCE), an expert of the GRISLI model, in interaction with the PhD student on the ANR Delta project (coordinated by Y