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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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described below? Are you our future colleague? Apply now! Education · A PhD in machine learning, AI, with a focus on application of AI on energy systems. Experience and skills · Strong
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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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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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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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PhD candidate in the automated detection of measurable residual disease in hematological malignancie
(deep learning, probabilistic modelling, generative AI) or machine learning Proficient in Python or R programming Strong communication skills in English Strong interpersonal skills Ability to work