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part. Your work may also include teaching or other departmental duties, up to a maximum of 20 per cent of full-time. Your qualifications You have graduated at Master’s level in applied mathematics
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level in machine learning, computer science, mathematics, statistics, physics, or a related area that is considered relevant for the research topic of the project, or completed courses with a minimum of
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advanced courses in Computer Science, Electrical Engineering, or Applied Mathematics. Alternatively, you have gained essentially corresponding knowledge in another way. The requirement for a degree must be
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Department of Crop Production Ecology The Department of Crop Production Ecology is part of the Ecology Centre and, together with the Department of Ecology, offers a dynamic and inspiring research environment. We develop knowledge about resilient and sustainable crop production systems to meet...
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transformations. The project investigates a hybrid approach that combines deep learning with grammatical inference to develop models that are interpretable, efficient, and mathematically verifiable while leveraging
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Electrical Engineering, Engineering Physics, Applied Mathematics, Computer Science, Communications or similar. Strong background in mathematical analysis (multivariable calculus, probability, linear systems
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strong background in mathematics and statistics. The applicant should be skilled at implementing new models and algorithms in a suitable software environment, with documented experience, as well as skilled
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experience in bioinformatics Experience working with cell cultures The position requires that you are cooperative, analytical (mathematical ability), and genuinely engaged in the subject. You should also have
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imaging, mathematical modelling, and functional genomics, receiving experimentally testable predictions generated by state-of-the-art predictive models. These predictions will be rigorously validated using
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infrastructure, and flexibility in delivery both in terms of time and location. You will use mathematical modelling and big data sets to analyze capacity limitations and different charging conditions, and how