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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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. 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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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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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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strong multi-disciplinary focus on energy markets, optimisation, game theory, control and machine learning. The EMA section (https://wind.dtu.dk/research/research-divisions/power-and-energy-systems
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teaching courses and co-supervision of BSc and MSc. Qualifications MSc graduates with a background in either engineering, mathematics, computer science, computer engineering, physics, sustainable energy
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Networks context. You are a committed individual with a strong background in engineering or applied mathematics / computer science, with a keen interest in scientific programming, machine learning and data
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background in engineering, with a keen interest in scientific programming, machine learning and reliability engineering. Your curiosity drives you to explore and understand the intricacies of wind energy
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Networks context. You are a committed individual with a strong background in engineering or applied mathematics / computer science, with a keen interest in scientific programming, machine learning and data
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background in engineering, with a keen interest in scientific programming, machine learning and reliability engineering. Your curiosity drives you to explore and understand the intricacies of wind energy