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-liquid crystal order in developing cross-striated muscle, or use machine-learning to expand existing custom-built image analysis pipelines (Python, Matlab). To learn more about this project, we highly
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defects. The charge transport will be implemented stochastically to mimic nature. A significant focus of the project will be to apply machine learning techniques to optimize the model and enable charge
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platform for cancer, in collaboration with experimental partners. Your tasks: Development and application of interpretable large-scale hybrid mechanistic- and machine learning-based mathematical models with
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have some knowledge and/or experience in several of the following topics (ordered by importance): Wireless Communication Technologies Distributed and Embedded Systems Machine Learning Data Analytics and
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such as deep learning and explore the registries to identify patterns of aging in health related datasets. The candidate will use natural language processing and large language models and other machine
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interest for the machine learning and neuroscience communities How to apply... Applications should include: Curriculum Vitae Cover letter Early application is highly encouraged, as the applications will be
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knowledge and/or experience in several of the following topics: Optimisation algorithms Machine learning algorithms Swarm intelligence Algorithmics Parallel/Distributed computing Space systems engineering
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discipline The ideal candidate should have some knowledge and/or experience in several of the following topics: Optimisation algorithms Machine learning algorithms Algorithmics Smart buildings Internet
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, or any related engineering discipline The ideal candidate should have some knowledge and/or experience in several of the following topics: (Quantum) Optimisation algorithms (Quantum) Machine learning
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for each position. PhD position in Advanced Lipid-Based Cellular Membrane Mimicry and Interaction Studies PhD position in Holographic Duality and Machine Learning PhD position in Next-Generation Nanomaterial