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sequencing, large-scale genomic, transcriptomic, proteomic, metabolomic, and phenotypic data) using cutting-edge technologies, such as machine learning You will perform transcriptomic and epigenetic analysis
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the understanding of femtosecond structural dynamics in quantum materials and 2D van der Waals systems. This position is available immediately. Requirements Successfully completed PhD degree in physics or a related
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at large scale facilities Establishment of cooperation projects with energy-related institutes at Forschungszentrum Jülich Initiating grant applications Supervision of MSc and BSc students Presentation
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working with ocean or earth system models, or similar models A background in analyzing large data sets and visualizing data using Python, MATLAB, or equivalent very good writing, presentation, and
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-reconstructions and observations, low-order data assimilation, or deep neural networks. A quantification of the impact of mesoscale and submesocale features is also expected. At a later stage, the successful
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journal articles Contribution to the overall support of the project, working group, and team collaboration Requirements: PhD in data science, or in any field with relevant experience Experience with
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associated with the Ultrafast Nanoscience group of Prof. Ralph Ernstorfer at TU Berlin. Requirements Successfully completed PhD degree in physics or a related subject. Excellent communication skills, and in
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us We are TUM’s unique Pathology AI lab developing new machine learning (ML) methods for automatically analyzing digital pathology data and related medical data. Such methods include the automatic
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using geographic information systems (GIS) and programming languages (e.g. Matlab, Python, R) and working with large data sets and data formats, such as netCDF, HDF, including analysis tools such as NCO
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with fewer data points and tailored reward functions towards design objectives while generating molecules in 3D. Additional requirements: Doctoral degree (PhD) in computational (medicinal) chemistry