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Field
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mandatory core focus, the PhD position allows room for the individual research interests of the applicant to shape specific aspects—whether in modeling strategy, applied machine learning methods
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Mathematics/ Approximation Theory to be filled by the earliest possible starting date. The Chair of Applied Mathematics, headed by Prof. Marcel Oliver, is part of the Mathematical Institute for Machine Learning
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-class graduates with expertise in the CRC-addressed PhD subjects, high interdisciplinary desire to learn and willingness to cooperate, openness for internationalization and diversity, very good verbal and
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, Machine Learning Frameworks, ROS etc.) are needed and demonstrated expertise in at least one of neural networks or intelligent robotics is needed for these positions. Prior publication in a relevant AI
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to electron transport theory, machine learning, and molecular dynamics simulations, providing opportunities for students to acquire a broad range of skills. The close interaction with the experimental group
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and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics; from data integration to data-related topics such as uncertainty
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for Astronomy in Germany. The StarForML group focuses on developing robust machine learning tools for the evaluation of star formation observations. We aim to gain new insights into how star formation progresses
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related discipline. Strong expertise in medical imaging and/or machine learning. Excellent programming and research skills. Interest in translational research and interdisciplinary collaboration with
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applications for a PhD Student or Postdoc Position (f/m/d) for any of the following topics: Combining non-equilibrium alchemistry with machine learning Free energy calculations for enzyme design Permeation and
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well as an active engagement in the 3RTG activities. Requirements: successfully completed university degree (Master's, Diploma or equivalent) and relevant PhD in Computer Science, Computer Engineering, or related