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data integration, cancer systems biology, and single-cell analysis, with strong links to clinical and translational oncology. Our work is based in the dynamic research environment of Heidelberg
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experience in robotic systems and automation, ideally with applications in precision or laboratory environments Experience with machine learning (especially reinforcement learning in image analysis) is highly
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infrastructure. The position entails active involvement in both experimental procedures and computational data analysis, alongside the improvement of advanced methodologies, including single-cell proteomics
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analysis, combinatorics, geometry, information theory, or stochastics. The new professor will be a member of the Faculty of Physics and Astronomy and the Faculty of Mathematics and Computer
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holistic approach between experimental data and model analysis, for which we employ an array of research methods and numerical simulations. Applicants will join an active research group with frequent chances
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at Heidelberg University, in particular connecting classical and quantum field theory and complex systems theory with analysis, combinatorics, geometry, information theory, or stochastics. The new professor will
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lines; generation of samples for proteomics analysis Data integration and analysis, including transcriptomic, proteomic and library screen data Target validation by CRISPR-Cas9 knockouts in primary AMLs
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systems Data analysis, interpretation, and collaboration within the CRC: bioinformatics-assisted interpretation of molecular and functional data, close interaction with collaborating groups Your Profile
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analysis of experimental data using appropriate computational tools. Ideally, the successful applicant will strike a balance between development and application of methods for spatial high-resolution imaging
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degree in (analytical) chemistry, biochemistry, or a related discipline Hands-on experience in mass spectrometry-based proteomics (e.g. DDA, DIA, PRM) Experience with proteomic data analysis Computer