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experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image data analysis A willingness to engage in interdisciplinary scientific work
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. RESPONSIBILITIES: Implementing laboratory and computational workflows for large-scale biological studies Collecting experimental and high-throughput data Characterisation of molecular and cellular mechanisms
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algorithms for microscopy image analysis problems (primarily 2D timelapse data), which are driven by real applications in life science research Developing solutions to integrate large foundation models
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Experience in programming (e.g. FORTRAN) Experience in the analysis of large data sets and/or the development of diagnostics Ability to work in a team, open-mindedness, and scientific creativity At a workplace
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RESPONSIBILITIES: Implementing laboratory and computational workflows for large-scale biological studies Collecting experimental and high-throughput data Characterisation of molecular and cellular mechanisms
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development of early warning systems, risk assessment of pathogens; optimization of the calculation of disease burdens, visualization of complex correlations, Big Data analyses, automated analysis of high
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Pathogens, a WHO Collaborating Centre, and a member of the Leibniz Research Association. The Computational Infection BiologyDepartment, led by Thomas Otto, is seeking a highly motivated PhD Student (in data
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, (bio)informatics, and multimodal data analysis. The research group led by Dr. Johanna Raidt focuses on the identification of known and novel MMAF- and PCD gene variants using large patient cohorts
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mechanisms and translate them into novel therapeutic strategies for neuroimmunologic diseases. The PhD Project involves: Planning and performing innovative large-scale experiments bridging human patient