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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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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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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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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
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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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modeling of complex information systems, and SDU and the Royal Danish Defence College’s established intelligence studies and practice. By introducing computational modeling to traditional intelligence
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linking the farm-level bio-economic model MODAM with macroeconomic Computable General Equilibrium (CGE) modeling approaches. We are offering a temporary part-time position (65% of regular weekly working
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linking the farm-level bio-economic model MODAM with macroeconomic Computable General Equilibrium (CGE) modeling approaches. We are offering a temporary part-time position (65% of regular weekly working