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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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:this project pioneers a new paradigm of General Genome Interpretation (GenGI) models by combining DNA Large Language Models (DLLMs) with Deep Neural Networks to predict human phenotypes directly from Whole Exome
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responsibility of developing predictive tools based on machine learning for the analysis and interpretation of Raman vibrational spectra applied to battery materials. The successful candidate will design and
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entanglement. - Theoretical and Analytical Studies: Conduct theoretical and numerical analyses of superradiant molecular ensemble models, with thorough documentation of processes and results. - Simulation Code
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methods to integrate transcriptional and cellular dynamics. Analyze large-scale transcriptomic and spatial dynamics datasets. Work in close collaboration with the team's biologists to test predictions from
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description The main objective of the project is to develop an instrument model to predict
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of patients undergoing orthognathic surgery, including cutting and repositioning of the maxillary and/or mandibular structures. The objective was to predict the aesthetic and functional consequences
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predict the results of high-fidelity characterization (which may require slow and/or destructive measurements during direct testing) from a set of faster, simpler, and non-destructive low-fidelity
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to the production and exploitation of large simulation ensembles for the assimilation of paleoclimate data over long climate time scales. · Participate in the scientific exploitation of the simulations produced and
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methods to understand and predict the adsorption, self-assembly, and protective behavior of N-heterocyclic carbenes (NHCs) on metallic and oxidized surfaces. NHCs are promising corrosion-inhibiting