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settings with limited data and transfer learning across species from human to mouse, and beyond. • Apply explainability methods to extract biological insight from trained models. • Keep up to date with
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facilities available in the C2N cleanroom. Electron-beam lithography, laser lithography, reactive ion etching, wet chemical etching, and metal deposition techniques will be employed. - low-temperature
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more than 150 staff members. It is one of the four constituent units of the Institut de Biologie Paris Seine (IBPS, FR3631), directed by Martin Giurfa. The unit is affiliated with UFR927 and the Faculty
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on developing deep learning methods for the reconstruction and physical analysis of ATLAS experiment data. The selected candidate will develop innovative analysis methods for the reconstruction and physical
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remains largely unexplored and represents a fascinating frontier for molecular computing. The development of new methods will be necessary to enable the manipulation of data within complex molecular
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. Conduct structural, morphological, and chemical characterization of metal–zeolite composite materials. Collaborate closely with the project partners within the LIZA consortium. Contribute to the development
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. Development and integration of state-of-the-art machine learning techniques in the analysis and event reconstruction will be a major component of this work. - Characterization of silicon detection modules using
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://igdr.univ-rennes.fr/ team-mitochondrial-biology-and-integration-with-the-cell-cycle) focuses on state-of-the-art methods and tools in quantitative fluorescence microscopy to study the spatiotemporal
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difference operators, our focus will be on a Fourier-space description of spatially periodic velocity fields, taking direct inspiration from Fourier pseudo-spectral methods used in standard DNS codes
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, ideally molecular dynamics and/or DFT. Scientific programming skills, particularly in Python, are expected. Familiarity with machine learning or generative AI methods applied to materials would be a strong