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Field
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subject areas include hardware, algorithms, visual computing, AI, databases, software engineering, information systems, learning technology, HCI, CSCW, IT operations and applied data processing
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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Machine Learning for Image Classification. Eligibility You must: We would like you to have: sound knowledge of machine learning, computer vision and image processing strong programming skills. How to apply
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Technology, Mathematics and Statistics, and Computer Science. More about the position Modern diffusion models underpin state-of-the-art generative AI for images and continuous data, yet principled diffusion
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for therapy. To do this, we interrogate the spatial relationships between B and T cell clones and their immediate niches within tissues using our in-house developed spatial transcriptomics-based technology
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degree in biomedical engineering, mechanical engineering or related relevant field A demonstrated and substantial experience with computational simulation methods, i.e. finite element analysis English
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Deadline 23 Apr 2026 - 23:59 (Europe/Warsaw) Country Poland Type of Contract Temporary Job Status Full-time Offer Starting Date 1 May 2026 Is the job funded through the EU Research Framework Programme? Other
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, Biomaterials, Biomechanics, Tissue Engineering, Computational Biology, Biomedical Imaging and Modelling, with 800+ students and 200+ academic staff. Eindhoven University of Technology is an open and inclusive
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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 16 days ago
Starting Date 8 Apr 2026 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Reference Number Researcher/FCT_SUPERATE/i3S/0804/2026 Is the Job related to staff
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background in neurophysiology / imaging and animal behavior to join the Whitlock group. The position is financed internally by NTNU and is part of a new line of work investigating the neural population coding