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networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with
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experience in: Deep learning Medical imaging computing (preferably neuroimaging) Computationally efficient deep learning Deep learning model generalisation techniques. Translating deep learning models
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, spatial, and social data into digital twin models for scenario testing and policy simulation. Adapt co-design methods to local contexts in demonstrator sites (Portugal, Sweden, Italy). Contribute to policy
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51 Faculty of Physics Startdate: 01.09.2025 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 31.08.2029 Reference no.: 4386 Are you interested in
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- Develop original numerical methods for facility simulation in presence of expansion waves - Demonstrate improved estimates of rate constants for two-temperature models - Contribute
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simulation, fabrication and experimental characterisation. The successful candidate will also be expected to contribute to the formulation and submission of research publications and research proposals, as
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that writes and reads these modifications. The project merges several disciplines, complements ongoing lab efforts, and expands upon our recent discoveries in this area. The postdoc will have access to several
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of tumour mouse models and assess the antitumour effect of this therapeutic method. The candidate should have the skills to develop patient derived organoids for orthotopic injection in mice. The candidate
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are entitled to at least 10 days per year (pro-rata) for professional development. This entitlement, from the Concordat to Support the Career Development of Researchers , applies to Postdocs, Research Assistants
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dedicated to development, translation and clinical application within medical imaging and computational modelling technologies. Our objective is to facilitate research and teaching guided by clinical