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to be part of a world leading research environment and contribute to the development for the next generation scientific machine learning tools for power systems. One PhD student will focus on physics
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Job Description The Section of Bioinformatics, DTU Health Tech is world leading within Immunoinformatics and Machine-Learning. Currently, we are seeking a highly talented and motivated PhD student
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. Experience with simulation tools, including Isaac Gym, Isaac Sim, Aerial Gym. Experience with ROS, and especially real-life aerial robots. Experience with open-source tools for deep learning, computer vision
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the laboratory. Modeling and simulation skills (batteries, energy systems, electric equivalent circuits). Machine learning, statistical analysis, and other contemporary data-driven techniques. Computational
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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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. The consortium consists of world-class scientists with competences spanning chemistry, biochemistry, computer science, and machine learning. All fifteen doctoral candidates will work with two research groups, and
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models and machine learning and text analysis using natural language processing will be an integrated part of the project hence knowledge, skills, and interest in these areas will be an advantage. We
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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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mechanisms by which enhancer or promoter dysregulation contributes to disease risk. We take an interdisciplinary approach and combine machine learning, statistical learning, genetics, and molecular biology to
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developing the skillset on underwater perception technologies within topics such as: Qualifications: A master's degree in computer vision, computer science, robotics, electrical engineering, or a related field