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Field
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Beginning Winter semester Application deadline All students – online application: 1 March for the following winter semester https://www.lmu.de/psy/de/studium/doctoral-training-program-in-the-learning-sciences
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problem. Do a theoretical analysis of the assigned problem using tools from information theory and related fields. Design a solution which is implementable on a computer and in hardware. Collaborate with
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fields. Design a solution which is implementable on a computer and in hardware. Collaborate with colleagues to implement the solution in hardware at the ACES lab at the Institute of Theoretical
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analysis, ideally using Python-based tools and frameworks, with familiarity in areas such as statistical modelling, machine learning, computer vision, or optimisation approaches for experimental systems
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excitations and excitonic effects using advanced Wannier-based methods * Quantum transport in polymer materials with electron–phonon coupling Full details and application instructions: https://www.ch.nat.tum.de
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check our website for more information: https://www.uni-goettingen.de/de/635183.html PhD students with their own funding (e.g. DAAD) can join at any time. Tuition fees per semester in EUR None Combined
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of a thesis proposal. If you have an MSc in Computer Science or a closely related field, you will be admitted directly to phase 2. More details regarding the admission can be found at https
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RPTU University Kaiserslautern-Landau • | Kaiserslautern, Rheinland Pfalz | Germany | about 1 hour ago
tailored to their needs. For further information, see: https://www.physik.uni-kl.de/oscar/ . Course organisation During the research work, the PhD student has the possibility to participate in lectures which
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experience with PyTorch. Ideally, knowledge in computer vision and object detection/segmentation. Motivation to independently delve into new and current research topics. Willingness to work with erotic
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embedded in the Bremen Research Cluster for Dynamics in Logistics (LogDynamics) (http://www.logdynamics.de ). LogDynamics is a cooperative network of research groups from five different departments