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organ transplantation. HLA-Epicheck is a predictive model of the antigenicity of polymorphic amino acids on the surface of HLA antigens, relying on dynamic structural data. Four tasks are identified. Task
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data in order to predict the effects of ionizing radiation on living matter and to contribute to the development of innovative radiotherapies. These developments are carried out within a multi-scale
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Theory of Large-Scale Structure (EFTofLSS)—to include the effects of massive neutrinos and non-standard dark matter, validate theoretical predictions against simulated datasets, forecast expected
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missions within the framework of this ERC: i) to develop new models for the geometry of the cosmic web, taking into account the bias of baryons, and ii) to implement “nulling” techniques to predict the weak
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nanoparticles, whose manufacture is generally based on “trial & error” methods. Thus, the aim of TOSCaNA is to develop an experimental approach and a CFD formalism for predicting the size and morphology of metal
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via protein crystallography. - Development of a pipeline for protein design with existing software. These includes software for fold generation, sequence generation and structure prediction, and should
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to compare theoretical predictions with experimental observations and measurements. - extend existing stochastic models and develop new models to understand actin dynamics in different contexts
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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 combine
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elements are frequently involved in horizontal transfers, allowing them to colonize new hosts. However, understanding and predicting how horizontal transfers shape the distribution of TEs among species is
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regulation during heat stress. The candidate will use AI structure prediction tools to examine HSF and DNA/nucleosome interactions, express and purify recombinant HSFs and structurally characterize HSF-DNA