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mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
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, synthetic biology, mathematical modelling and AI/ML and more to design the next generation microbial cell factories. We do this with “the end in mind,” meaning that we have a commercial and industrial
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equivalent to a two-year master's degree. Your academic background needs to be relevant to the above-stated project objectives, e.g., civil engineering, mechanical engineering, physics, or applied mathematics
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Mathematical, Probabilistic and Engineering principles and methods. Familiarity with offshore engineering and wave-wind theories is advantageous. Experience in coding (e.g., Python) and in the use of Structural
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problem-solving skills, with a solid foundation in Mathematical, Probabilistic and Engineering principles and methods. Familiarity with offshore engineering and wave-wind theories is advantageous
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structured and mathematical mind-set, and a self-motivated drive to excel in research. Prior experience with research in photonic crystals, band topology, or symmetry analysis is an advantage. Only candidates
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within chemistry, physics, medical biology, molecular biology, environmental sciences and mathematics. Bachelor programs are offered within these areas, as are interdisciplinary Master programs
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Nonlinear Structural Analysis Strong analytical and technical problem-solving skills, with a solid foundation in Mathematical, Probabilistic and Engineering principles and methods Familiarity with offshore
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Job Description We are looking for an ambitious biologist or veterinarian with an interest in diet, intestinal microbiome and chronic kidney disease. You will become part of our internationally renowned research group for Gut, Microbes, and Health at the National Food institute, Technical...
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– Department of Mathematics and Computer Science – is an internationally recognised academic environment with over 400 employees and 10 research sections. We broadly cover digital technologies within mathematics