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the flexibility and power of NNs with the ability of LMMs to robustly learn from structured and noisy (non i.i.d.) data, applying them on the prediction of both plants and human phenotypes. These models will
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 12 days ago
prediction (e.g., AlphaFold2), allosteric signaling remains poorly understood, largely due to the scarcity of dynamic data. Our group recently developed: DynaRepo, a database of molecular dynamics trajectories
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functioning will be supported by several EU projects (participation to congress etc..). - main mission: He/she will develop a new generation of predictive models incorporating abundance distribution across size
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worldwide. As part of the national CARDIO-LAMIN project, we are recruiting a Post-Doctoral Researcher whose work will focus on the identification of mechanistic biomarkers and risk prediction in
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by the CNRS, the postdoctoral researcher will be responsible for contributing to the development of advanced methodologies for predicting crystal structures (CSP) based solely on their chemical
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17 Sep 2025 Job Information Organisation/Company Université de Caen Normandie Research Field Neurosciences Researcher Profile First Stage Researcher (R1) Positions Postdoc Positions Country France Application Deadline 18 Oct 2025 - 23:59 (UTC) Type of Contract Temporary Job...
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applying bioinformatics tools capable of predicting protein aggregation and co-aggregation, followed by large-scale scanning of microbial proteomes against the human proteome. The objective
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at the University of Munich, SAW transducers will be integrated with waveguide structures to perform spectroscopy on a small inhomogeneous ensemble (the implanted region) and to provide direct evidence of acoustic
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algorithms that are robust to prediction errors, while still performing well when the prediction is accurate. Where to apply E-mail job-ref-o6ncfux8o1@emploi.beetween.com Requirements Research
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feature filtering procedure to deal with the large feature set necessary to predict the thermoelectric ZT of a material. - Improve the already existing experimental dataset. - Apply different machine