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Field
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in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning, particularly deep learning and optimization methods Excellent
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of topics is covered, from large-scale data management to data mining and data analytics (including machine learning and deep learning); from high-performance computing to high-performance analytics
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student projects and BSc/MSc theses Your Profile: Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field Strong background in machine learning
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, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication networks or power
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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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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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play a central role in this interdisciplinary initiative. They will: Develop and apply machine learning (ML) methods – including surrogate modeling, feature extraction, and inverse design algorithms
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, R) Expertise in machine learning, Bayesian statistics is beneficial Capacity for interdisciplinary teamwork and excellent communication skills Ability to communicate in English fluently
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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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, the student will collaborate with researchers who apply data assimilation and machine learning methods to the developed models. Your responsibilities: Analysing a global compilation of paleomagnetic sediment