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future scenario simulation of VBD Including machine learning, statistical, and process-based models Present findings at scientific conferences and publish in peer-reviewed journals Contribute
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future scenario simulation of VBD. Including machine learning, statistical, and process-based models. Present findings at scientific conferences and publish in peer-reviewed journals. Contribute
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hosts. The project is centered on the integration and analysis of multiomics datasets utilizing advanced machine learning approaches and biological network analysis. The successful candidate will join an
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project BEPREP (f/m/d) Your tasks: Apply and further develop machine learning methods for the analysis of health and climate data Conduct spatio-temporal analyses of patient and climate datasets to identify
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approaches and will integrate novel hardware (including electrode arrays, microdevices, analytical systems) into automated robotic pipelines You will also apply machine learning-based analyses to imaging and
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for Astronomy in Germany. The StarForML group focuses on developing robust machine learning tools for the evaluation of star formation observations. We aim to gain new insights into how star formation progresses
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Limitation:Temporary (2 years) Contract:TV-L Your tasks Develop and implement computational pipelines for processing and analyzing ONT RNA/cDNA sequencing data. Apply machine learning and signal processing approaches
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Organoid Engineering for Multi-Organ Interaction Studies (POEM) program ( www.uni-heidelberg.de/en/cctp-poem ) brings together expertise in material science, computer science/machine learning, biophysics
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-scale controllable, and cost-efficient disease models by bringing together experts in physical chemistry, physics, bioengineering, molecular systems engineering, machine learning, biomedicine, and disease
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, and therapy resistance mechanisms Ability to work independently and collaboratively within interdisciplinary teams Prior experience with network modeling or machine learning is a plus We offer