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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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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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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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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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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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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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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
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physiological parameters An interest in data analysis and AI Collaborating with the POEM platform head as well as other postdocs and research groups in the interdisciplinary POEM network Integrating the findings