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that contribute to new breeding tools and accelerates discovery. The position will offer opportunities to explore several approaches to solve the optimisation strategies including simulation-based, big data
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Statistics is offering a postdoctoral scholarship within the project “Phase transition in aggregation processes and network models”. The scholarship is full time for two years with starting date 1 January
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cities in implementing effective climate action strategies through spatial planning. The appointee will be responsible for leading the modelling work on this project and coordinating the research and
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Fluorescence microscopy Generation of antimicrobial formulations In vivo testing in mouse models Previous work experience with the pathogen Chlamydia is beneficial but not required. Given the collaborative
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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 Job description In recent years, AI models have shown remarkable
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medicine, with a particular focus on spinal cord and peripheral nerve injuries. The projects involve work on various aspects of nerve regeneration and tissue engineering using both in vitro and in vivo model
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radar altimetry data tracking surface water variations. AI models will be used to analyze complex surface water-groundwater feedback loops, simulate future scarcity under climate change, land use, and
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vehicles. The Postdoctoral researcher is also expected to develop mechatronic concepts for rail vehicles and implement these ideas as simulation models to validate the concepts. The projects you will work
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genome editing approaches, as well as co-culture systems modeling neuron–glioma interactions, with the main focus on investigating the impact of neuronal activity on 3D genome organization and cellular
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for applications in virtual reality, gaming, digital assistants, and social robotics. We build on recent breakthroughs in spontaneous speech synthesis and gesture generation based on deep generative models to train