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it fit to run on a supercomputer. You will apply your newly developed simulation tools within different research projects. This new position is embedded in the international research project LION 2 on
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on studying shape parametrization, learning gait optimization functions for mechanism design and using different machine learning embeddings (such as GANS, VAEs, and Diffusion Models) for developing a new full
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with large language models (LLMs). The successful candidate will investigate both theoretical aspects – such as understanding the mechanisms and limitations of reasoning in modern LLMs – and practical
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and backtests to assess model performance and estimate the tool's real-world impact. You will have regular check-ins with the project team at Stanford but will conduct the day-to-day data work yourself
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, at the phonetic, lexical and syntactic levels. The candidates will develop theoretical models of network of single neurons, using dynamical systems theory and simulations. The models will be fit to single-cell
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collaboration with key disease biology experts in a strong internal and external network. You enable the generation of state-of-the art cellular models and cell-based assays through your scientific excellence
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(CPU/GPU), numerical modeling/Monte Carlo simulations are an asset Visualisation skills are an asset Careful way of working, checking of results Candidates can have an M.Sc. degree in STEM, or a Ph.D
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the eDIAMOND project, namely: Distributing model training and inference over a network of resource-constrained devices. Online, context-aware adaptation of Federated Neural Network Architectures based