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. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be made by two
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). The position is a full-time position and is based at DTU Lyngby campus, just north of Copenhagen. The position is part of DTU’s Tenure Track program. Read more about the program and the recruitment process here
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to enhance efficiency, power density, and performance in applications such as high-performance computing (HPC) systems and artificial intelligence (AI) accelerators. Responsibilities and qualifications As a
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technologies of the department, e.g., batteries and catalysts. The project includes collaboration with experimentalists at DTU, who will verify the computational predictions as well as Saltfoss Energy, our
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approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see
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our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education . Assessment The assessment of the applicants will be performed by Professor
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documented experience in at least one and preferably more of the following areas: computational solid- and/or biomechanics; finite strain hype elasticity; finite element methods; numerical optimization methods
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Investigator program “Power efficient fiber-optic communication (POPCOM). The position is focused on developing machine learning frameworks, in terms of constellation and pulse-shaping, as well as equalization
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be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD
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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