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computational costs by orders of magnitude and enabling breakthroughs in drug design and materials science. The position bridges machine learning and molecular science, with opportunities for collaboration
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data-driven models for complex data, including high
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, machine learning, and bioinformatics tools. Expertise in CRISPR-based assays, especially CRISPR screening, is highly meriting, as is experience with single-cell RNA sequencing or other omics assays
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fluids, flow-induced pattern formation in both simple and complex flows (e.g. flow instabilities, product defects), multiscale analysis, and the application of machine learning techniques. About the
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clinical service, appointments of trust in trade union organizations, or similar circumstances. Doctoral degree should be within bioinformatics, machine-learning, computational biology, genomics, or a
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interdisciplinary and to learn new skills and to perform research in collaboration with others. We seek candidates with the following qualifications: A doctoral degree in a Bioscience-related field awarded
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include absence due to illness, parental leave, appointments of trust in trade union organizations, military service, or similar circumstances, as well as clinical practice or other forms of appointment
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description
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and Data Science for Spatial Genomics in Diabetes This position centers on the development and application of machine learning, image analysis, and integrative omics approaches to spatial
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great advantage: Forest and wood production processes Wood construction Furniture manufacturing Wood material science Machine learning Process simulation and optimisation The postdoctoral fellow is part