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phenomena (nanoscience and size-dependent fundamental properties) Soft matter and physics–biology interfaces (complex systems, living tissues, liquid crystals, foams, polymers, granular matter). The PhD will
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states of matter • Physical phenomena with reduced dimensions • Soft matter and the physics-biology interface The first area groups together both experimental and theoretical studies relating
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genomic data analysis is desirable but not essential. - A strong interest in applications in biology and ability to work in a multidisciplinary team Additional Information Work Location(s) Number of offers
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(LCSB) is an interdisciplinary research centre of the University of Luxembourg. We conduct fundamental and translational research in the field of Systems Biology and Biomedicine - in the lab, in
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architectures. Application domains include, among others: meteorology, climatology, aeronautics, automotive, astrophysics, high-energy physics, materials science, energy production and management, biology, and
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ecological). Specific questions will be asked about perceptions of environmental changes and their impacts (species displacement, abundance, biology, unusual events). The recruited person will be responsible
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chemistry of polysaccharides, biology, microbiology, glyco-chemistry). The person will be supervised by Dr. Laurent HEUX from the SPG team, which focuses on the characterization, modification, and design of
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. This PhD will be affiliated with the “Soft Matter and Physics-Biology Interface” area, specifically within the “TICE” team (Tissues, Cells, and Biofilms). Both laboratories are equipped with most
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28 Aug 2025 Job Information Organisation/Company CNRS Department Centre d'Énergétique et de Thermique de Lyon Research Field Engineering Chemistry Physics Researcher Profile First Stage Researcher (R1) Country France Application Deadline 17 Sep 2025 - 23:59 (UTC) Type of Contract Temporary Job...
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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning