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learning models for digital phenotyping and genomics Work with multimodal datasets (images, 3D data, motion, genomics) Implement models in Python (e.g. PyTorch) using high-performance computing
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applications. The project is partially funded by AMD, and the successful candidate will collaborate with AMD researchers. As part of this research, you will: Investigate how different AI tasks perform on AMD
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ponds of different types and ecological quality, by implementing an integrative approach using conventional, molecular and computational tools to generate a dataset that spans multiple trophic layers
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different areas of AI (such as machine learning, computer vision, natural language processing, and bioinformatics), hosting powerful computing facilities internally and as part of the EU EuroHPC supercomputer
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Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Mass spectrometry imaging (MSI) enables spatial characterization
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will receive interdisciplinary training in bioinformatics, evolutionary genomics, and high-performance computing within the DDLS data-driven life science framework. Data-driven life science (DDLS) uses
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engines will no longer rely solely on centralised on-board computers; but will leverage distributed, multi-layered control architectures and off-board computational power to optimise performance in real
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diagnosis, and therapy of diseases like cardiovascular diseases or cancer. Overall, the institute strives to advance precision medicine by combining knowledge from different fields such as biology, chemistry
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. The combination of GaN’s wide bandgap, high electron mobility, and semi-insulating properties results in improved performance, making vertical GaN devices an ideal choice for next-generation power systems. In
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of polyploid genome evolution across contrasting timescales. The student will receive interdisciplinary training in bioinformatics, evolutionary genomics, and high-performance computing within the DDLS data