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Field
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-driven techniques for the generation and exploration of complex, large-scale scientific data. Publishing research in leading peer-reviewed journals and conferences. Researching and developing parallel
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Science, Computer Science, Applied Mathematics and Statistics, Electrical and Computer Engineering, Biomedical Engineering, or a related field. Experience with a deep learning framework like PyTorch. Strong
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of this grant. In addition, those researchers whose place of residence, at the time of their application, is Tenerife will not be able to benefit either. Selection process The selection process consists of two
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available. In parallel, we prospectively collect skin biopsies from well-phenotyped Parkinson patients (ProPARK cohort). We generate well-defined iPSC. Using advanced genomics and proteomics we aim
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, Mixture-of-Experts; distributed training/inference (e.g. FSDP, DeepSpeed, Megatron-LM, tensor/sequence parallelism); scalable evaluation pipelines for reasoning and agents. Federated & Collaborative
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-driven experiment design. Optimize pipelines for performance, parallelization, and near real-time operation during beam time. Contribute to simulation tools to test imaging concepts, predict performance
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collaboration with leading industrial and academic partners within large-scale Horizon Europe initiatives, where LTU plays a key role in both technical development and scientific coordination. In parallel with
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relevant) and needs to be passionate about research. Experience in usage Gaussian, ADF, Dalton, ORCA etc. software and parallel computing is required. Skills in data processing and visualizing the results
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studied. In parallel, mechanical tests at the material scale, conducted by project partners, will be modeled using finite element methods (Cast3m software). These models will then be used to reproduce
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permeability selection in variant membrane chemistries. This work will run in parallel to experimental analysis conducted at the University of Exeter using synthetic vesicles to observe permeability