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                in advancing the clinical and commercial translation of a cutting-edge multimodal endoscopic platform. As part of the Label-free Bioimaging Laboratory, the successful candidate will contribute high 
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                . Experience implementing, optimizing, or integrating quantum libraries such as Itensor, CUDA-Q, Qiskit, or PennyLane. Experience debugging and profiling distributed-memory parallel applications. Knowledge 
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                imaging and spectroscopy. Experience with data analysis or the ability to perform basic biochemical work with proteins and DNA, e.g., fluorescence labelling, enzymatic reactions will be highly rated in 
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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