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questions in the areas of self-supervised/label-efficient learning and explainability of deep neural networks (XAI) are being developed, particularly for use in biomedical applications. Further information
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computer architecture Parallel programming of HPC/AI accelerators (CUDA, OpenMP, SYCL, etc.) Salary Gross annual: €39,224 Application Deadline 07 October 2025, 12:00 (CET) via University of Trento
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-molecule TIRF microscopy, biochemical reconstitution, and fluorescent labeling, we will watch individual RNA molecules and degradosome complexes at work, capturing millisecond-scale dynamics that shape gene
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aims to push the limits of label-free mass measurement of biomolecules in solution by integrating advanced optical techniques with state-of-the-art data analysis. Building on recent developments in mass
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highly multidisciplinary environment that spans fundamental discovery through to patient care. Our researchers benefit from outstanding core facilities, including state-of-the-art label-free bioimaging
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. Knowledge of systems programming skills and concepts Experience with fundamental OS and storage concepts. Experience with programming heterogenous architecture. Preferred Qualifications: Experience with CUDA
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(based on biomaterials and living cells), including their efficient deployment on embedded hardware, as well as the design and management of the supporting data pipeline. You will be part of
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++ and CUDA) Code verification and validation of simulations Providing simulation results to project partners and customers Active participation in project, group and lab meetings, knowledge transfer
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to the clinic The post holder will be based in the Department of Biomedical Computing as part of the School of Biomedical Engineering & Imaging Sciences, King’s College London, a vibrant community of engineers
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Research (NWO), at the Laboratory of Phytopathology of Wageningen University. In this project you will use the advanced TurboID-based, proximity-dependent labelling technique to identify novel resistance