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Field
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are fulfilled A full-time position (38.5 hours/week); part-time employment may be possible within the framework of certain working time models Access to state-of-the-art single-cell and spatial omics
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evolution and rarely align with climate model outputs. These models also exhibit biases due to the omission of certain physical processes, and their spatial resolution is often too coarse to be functional
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sustainable management of European ecosystems. One position center on spatial prioritization for integrated land-use planning; the other on statistical and process-based ecological modeling of species and
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28 Jul 2026 Job Information Organisation/Company Fondazione Bruno Kessler Research Field Other Researcher Profile Other Profession Positions PhD Positions Application Deadline 27 Aug 2026 - 23:59
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago
the distribution, abundance, diversity, and interactions of marine organisms across spatial and temporal scales, and who examine how these dynamics influence marine ecosystem function, resilience, and global
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learning with mechanistic models; foundation models of genome regulation using single-cell and spatial multi-omics data; AI-based modeling of protein structure and protein interaction networks; AI-based
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mechanistically using an epithelial cell model. This interdisciplinary project provides training in developmental biology, bioinformatics, spatial transcriptomics, cell culture, imaging and molecular genetics
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About the Role Applications are invited to join an ERC-funded research programme. The successful candidate will work at the interface of bioelectronics, mechanobiology, and disease modelling
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Group Leader and Professor AI in Biology - Dept of Computer Science and Dept. Electrical Engineering
spatial multi-omics data; AI-based modeling of protein structure and protein interaction networks; AI-based modeling of cell morphology and tissue function using imaging and computer vision; AI models
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Biology: Development of deep learning, graph neural network, generative AI, and RNA foundation models to analyze large-scale omics, spatial transcriptomics, ribonomics, and imaging datasets. Application