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Field
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Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Tilburg School of Humanities and Digital Sciences, Center
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, … Experiences in specific application domains and their visualization needs, especially related to the two DISA-groups of data-intensive digital humanities and eHealth, are an asset in the assessment process
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transcriptomics data analysis. Experience in quantitative image analysis, computer vision, or digital pathology. A strong background in cancer biology or immunology. Experience with machine learning, deep learning
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this position, an ideal candidate should have: Completed a relevant PhD degree in Electrical Engineering, Computer Engineering, or any other related field relevant to the research; Good understanding of digital
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Electronics for biosensing (including analog/digital circuit design and signal processing) Implantable or wearable devices Optical and electrochemical biosensing techniques Experience working in
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and document analysis. Explore what data are available on essential health resources and supporting systems, and assess their usefulness. Co-create a regional risk picture by facilitating workshops
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. Qualifications: PhD in machine learning, with experience in applications in computer vision or medical image analysis. Strong publication record in top venues (e.g., CVPR, MIDL, MICCAI, IPMI, PAMI, TMI, MIA
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Computer Engineering (CE) section of the Quantum & Computer Engineering (QCE) department is looking for a highly motivated PostDoc candidate who wants to work on efficient and reliable digital CIM-based AI
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assess their usefulness. Co-create a regional risk picture by facilitating workshops to prioritise event–disruption–risk combinations with compounding impacts on care (e.g., precipitation extremes
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systemic infectious processes, immune responses, and evaluating therapies, refinement measures to reduce stress and improve welfare in experimental animals are underexplored. In this 3RTG, we will address