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Field
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high-resolution, quantitative time-lapse soil property measurements using high-performance, parallel computing. Together with our existing rich dataset, we will inform a soil-plant digital twin, enabling
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for coordinating activities and deployment of patient safety actions/methods with an assigned group of Clinical Service Units/business lines and intervening on safety events, risks or threats, as assigned. In
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from nanometres to millimetres. Develop numerical methods to capture droplets evaporative behavior accurately Compare and validate numerical results with experimental data from both literature and in
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technical knowledge in areas such as: Foundational Models Algorithmic Research Machine and Deep Learning Computer Vision Edge AI, TinyML, and Embedded AI Explainable AI Safe AI Federated, Parallel
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of parallel programming and experience developing methods for 2D and 3D problems are critical. Experience working with open source software frameworks and/or using modern open source code development
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you an engineer who wants to contribute to the high
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substantial knowledge and research experience in areas such as computational fluid dynamics, turbulence modeling, data-driven methodologies, machine learning, and parallel computing. The candidate should also
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completion) in applied mathematics, computer science, or a closely related field. Strong background in numerical linear algebra, algorithm design, and parallel computing. Proficiency in programming languages
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of numerical quantum many-body methods to study model Hamiltonians. Strong background in linear algebra. Preferred Qualifications: Experience with density matrix renormalization group and tensor network
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the project, the PhD student will become part of a team at DTU with numerical and experimental expertise in photonic computing. The activities within the project will benefit from synergies with other