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modeling and computational workflows Knowledge about machine learning: statistics and deep learning Experience in data analysis, visualization and presentation Good programming skills in languages such as
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level of independence, project outcomes and impact, and references in terms of publications, theses, technical reports, simulations, analysis, code samples, or documentation (multiple references
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, and references in terms of publications, theses, technical reports, simulations, analysis, code samples, or documentation (multiple references are considered beneficial). An evidence-based CV
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of publications, theses, technical reports, simulations, analysis, code samples, or documentation (multiple references are considered beneficial). An evidence-based CV that reflects a representative array of
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software (e.g., LLM agents for finding and fixing bugs) Static and dynamic program analysis (e.g., to infer specifications) Test input generation (e.g., to compare the behavior of old and new code via
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within the Institute of Theoretical Computer Science at TU Dresden. The main research area is the design and analysis of algorithms and data structures, with possible focus areas including randomized
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on the analysis and interpretation of the GNSS observations as well as on the application of geological and glacial-geomorphological findings for the reconstruction of ice-load history in Antarctic regions
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and Marine Research. The tasks include: scientific advancing methods of satellite altimetry analysis over the Antarctic Ice Sheet analysis of measured surface elevation changes in conjunction with
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areas: scientific programming and data analysis (e.g., Python, R, C++, MATLAB), computational modeling, imaging and sensor data processing, bioinformatics, systems biology, or biophysics Familiarity with
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on a single cell level. This includes single-cell RNAseq datasets and analysis tools, large high-dimensional flow cytometry phenotyping panels and a collection of transgenic mouse models specifically