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Field
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activity (work, studies, etc.) in Germany for more than 12 months in the last 36 months Master’s degree in physics, electrical/electronic engineering, computer science, mathematics, or a related field
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a doctorate. We are looking for: candidates with a Master’s degree in mathematics or a closely related field and with a strong background in probability theory. Prior knowledge in spatial stochastic
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to start as soon as possible at the Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence with the topic of responsible human-AI collaboration. The successful candidate will
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your suitability with evidence of the following: Have backgrounds in computer science (or engineering), system engineering, or physics/mathematics. Knowledgeable in machine learning techniques (had
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for rigid systems or require extensive sensing and computation. This project addresses the challenge of developing model-based control strategies tailored to soft bodies, capable of exploiting their compliant
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equivalent) in computer science, data science, applied mathematics, physics, materials science, or a related field Prior experience in computer vision, deep learning, or signal processing; familiarity with
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writing and presentation skills. At the start of the PhD, having obtained a Master’s degree in a relevant field, such as AI, mathematics, physics, (computational) neuroscience, etc.. Terms and conditions
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cross-disciplinary research initiative involving both computer and material scientists, providing excellent opportunities for practical impact by taking the outputs from the developed machine learning
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. The successful applicant will join the Biomedical Signals and Systems (BSS) group of the department of Electrical Engineering at the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS). As
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academic approval, and the candidates 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