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The Faculty of Electrical Engineering and Computer Science, Leibniz University Hannover | Hannover, Niedersachsen | Germany | 29 days ago
development of intelligent systems capable of learning from their environment, interacting, and solving complex problems independently. Close cooperation with the institutes of the faculty as well as an active
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on research fields, such as Neurosensory Science and Systems (University of Oldenburg) https://uol.de/en/phd-neurosensory-science-2 Auditory Sciences: Physics and Engineering, Physiology and Therapy of Hearing
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/ part-time full-time Programme duration 6 semesters Beginning Only for doctoral programmes: any time Application deadline No deadlines For details, see here: https://www.gradschool.physics.uni-bonn.de
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electronically at https://www2.mathnat.hu-berlin.de/NumAnaParDE in one single pdf-file. Since we will not return your documents, please submit copies in the application only. Your application must include a
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processes, in order to explain their long-term developments and to understand how they resonate into the present (https://www.uni-kiel.de/en/cluster-roots ). ROOTS combines expertise from a wide array of
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months In addition to exciting tasks and a collegial working environment, we offer you much more: https://go.fzj.de/benefits We welcome applications from people with diverse backgrounds, e.g. in terms
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machine learning Perovskite-based materials for thin-film transistors, memristors, light-emitting diodes, solar cells, and quantum computing The qualifications and skills required to head an institute
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The Center for Molecular Bioengineering ( B CUBE) ( https://tu-dresden.de/cmcb/bcube ) and its partner institutions, the Biotechnology Center (BIOTEC) and the Center for Regenerative Therapies
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the school context in particular,social work makes a significant contribution to socially securing educational processes,supporting transitions, reducing disadvantages and creating inclusive learning
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classification systems (RISE, GradCAM, EigenCAM, Integrated Gradients, ViT Shapley, etc.) Implementation and execution of experiments in the field of machine learning (ML): Cleaning, preparing, splitting