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Apply online now: https://karriere.klinikum.uni-heidelberg.de/index.php?ac=application&jobad_id=26749 PhD position in biomedical research (m/w/d) Stellenanzeige merken Stellenanzeige teilen wanted
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PhD in a relevant field (e.g. political science, statistics, computer science, informatics, economics, or related discipline) with a demonstrated focus on forecasting, statistical modelling, and/or
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transplant rejection through cutting-edge spatial multi-omics and computational metabolic modeling. The role involves developing and implementing computational methods to integrate single-cell and spatial
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Position as Computational Analyst / Bioinformatician in RNA Therapeutics and Cardiometabolic Disease
of (micro)RNA biology, 3D human model systems, nanomedicine, and computational (ML) disease modelling. You will be able to contribute to our vision to translate basic findings into medicinal RNA approaches
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the spatial and temporal transferability of the models. • Projection of the relationships obtained in future climate scenarios using ADAMONT projections to assess how gravitational crisis episodes will evolve
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developing approaches to leverage spatial data to better understand evolutionary histories. More information about the lab and their work can be found by visiting https://federlab.github.io/ About the
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the pharmaceutical sciences. More information about the department and its activities is available at uu.se/farmbio. The infrastructure platform “Spatial Biology” at SciLifeLab is currently in a strong phase of
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; enrolled in a PhD program or a non-degree course. Experience in: a) ROS, programming in C++ and Python; b) Modelling and control. Work Plan: The work plan is divided into five phases: Phase 1 – Development
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the services nature provides to people. The position will combine ecological data analysis with statistical and spatial modeling to quantify chemical impacts across multiple levels of biological
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leverage large-scale AI models to: integrate heterogeneous EO data sources, such as satellite, aerial and in-situ data, across spatial and temporal scales; enable zero-shot or few-shot learning for rapid