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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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to reconstruct the tree-of-life on Earth, it allows us to reveal how biological function has evolved and is distributed on this tree, and it is the foundation that enables us to use model organisms
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environment will provide you with unique development opportunities. This project is a part of a larger project which will involve two other postdocs and two PhD students in the coming years, within a Wellcome
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, economics, environmental science). You will bring expertise in food systems modelling, supported by a strong technical background that may span areas such as data science, input–output analysis, applied
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-edge projects in random growth models, the KPZ universality class, and probability theory. The successful candidate will work closely with Dr Busani and collaborators within the Maxwell Institute
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About the Role This is an opportunity to work as part of the team and project “Development of Multi-Modal Foundational Models and AI Accelerators for Zero-shot Intelligent Surveillance System
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megakaryocyte cell communication and coordination. We will employ a range of approaches including advanced microscopy (confocal and intravital), models of thrombus formation (ex vivo and in vivo ), and flow
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50 Faculty of Life Sciences Startdate: 01.09.2025 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 31.08.2031 Reference no.: 4268 Explore and teach
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47 Faculty of Psychology Startdate: 01.11.2025 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 31.10.2031 Reference no.: 4495 Explore and teach
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37 Faculty of Business, Economics and Statistics Startdate: 01.10.2025 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 30.09.2028 Reference