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Skip to main content. Profile Sign Out View More Jobs Postdoc in Computer Vision with Deep Learning for Material and Computational Design – DTU Compute Kgs. Lyngby, Denmark Job Description Do you
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uncertainty, advanced skills in programming, deep learning and/or experience with case studies in green chemistry, early-stage alternatives assessment or safe and sustainable by design (SSbD) are a clear
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. Understanding of programming, particularly in deep learning tools such as PyTorch and TensorFlow. Curious about or previously exposed to interdisciplinary research. Who we are BTECH is part of Aarhus BSS, Aarhus
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-mentioned topics. The qualifications of the successful candidate will preferably include experience with deep learning for agriculture-related perception tasks, as well as experience with the collection and
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: [email protected] Application due: 30/06/24 Application due: 6/30/2024 WE TRANSFORM SOCIETY WITH BUSINESS CBS is a globally recognised business school with deep roots in the Nordic socio-economic model. We
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2024. Apply online CBS is a globally recognised business school with deep roots in the Nordic socio-economic model. Our faculty has a broad focus on societal challenges, and we have earned a reputation
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of machine learning methods for reduced order process model generation (experience with Gaussian process models is further desirable). Developing APIs and automated workflows for connecting numerical
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Nordisk Foundation Data Science Collaborative Programme,“Synthetic health data: ethical development and deployment via deep learning approaches(SE3D)” which is a collaboration between Head of Center and
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-described and understood. This role will work initially on learning deep generative models to interpolate and extrapolate in a physically correct way by training it not only on magnetic field simulations but
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of the four integration dimensions. The research will consist of a combination of quantitative and qualitative methods. The ideal candidate for the position will be a recent PhD graduate with a deep knowledge