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interface of biology and physics, integrating mechanobiology, quantitative imaging and transcriptomics in organoids and tumors. Located within the largest cancer hospital in Europe, our lab benefits from
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at the Laboratoire de Physique de l'ENS, within Team 23, Materials for Energy from Advanced Modeling, in a strongly interdisciplinary environment at the interface between atomistic simulation, artificial intelligence
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/the-computational-brain/ ** Duration and Dates ** * Initial duration: two years * Extension possible * Full-time post * Preferred starting date: October 2026 to January 2027 ** Project description Learning and
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at the interface of cognitive neuroscience, neurophysiology and data science. Profile: PhD in neuroscience, psychology, cognitive science, statistics, data science or a related field, with strong
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science, at the interface between chemistry, biology, and health. Its research focuses in particular on the synthesis and design of bioactive peptides, foldamers, protein–peptide interactions, and
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following skills: Strong interest in the field of neuroimaging, psychiatry and genetics. Computer skills: Strong level in the main informatics software (FSL, Freesurfer, fMRIprep) and coding languages (R
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specialists in magnetic nanoparticles, water remediation, and CO₂ capture. This position offers an opportunity to develop expertise at the interface of plasma processes, surface engineering, nanomaterials, and
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engineers and technicians, 31 PhD students and 16 postdoctoral researchers. The laboratory's activities lie at the interface between fundamental academic research and industrial research. Over the years, LTM
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institutes for fundamental research in condensed matter physics, with interdisciplinary activities at the interfaces of chemistry, engineering, and biology. The laboratory is affiliated with the CNRS Institute
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Inria, the French national research institute for the digital sciences | Villeneuve la Garenne, le de France | France | 2 months ago
simulation, shape/topology and system-level design optimization applied to deformable systems — advanced level Data-driven design and modelling approaches (machine learning applied to physical/mechanical