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to interact and collaborate to develop robust ways to decode single molecule imaging data. Your profile The candidate should hold a PhD in biophysics, chemistry, nanoscience or related subjects and have a
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and maintenance of monitoring buoys and related sensor systems. Apply image analysis and machine learning techniques to ecological datasets. Develop and implement multi-platform monitoring frameworks
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combining CRISPR-based endogenous protein tagging, advanced quantitative imaging, and biochemical approaches, this project seeks to uncover novel principles of replisome plasticity. A key long-term objective
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be used to prepare lamella samples for high resolution cryo-EM imaging and tomography. From AI assisted image analysis, 3D models for key proteins and biomolecular complexes will be fitted into 3D
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to prototype validation and measurement activities. Document design choices, trade-offs, and experimental results in high-quality publications. The position offers the opportunity to establish an independent
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hybrid models that integrate limnological knowledge into machine learning models following the paradigm of Knowledge-Guided Machine Learning (KGML). The position is part of an on-going project
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will have completed a PhD in Assyriology no later than 4 years from the date of application (i.e., a PhD age of maximum 4 years in total, not including official leave and similarly). Furthermore