30 computer-vision-and-machine-learning-"https:" PhD positions at University of Southern Denmark
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. Qualifications and Expectations Applicants must hold (or be close to completing) a Master’s degree in biomedical engineering, mechanical engineering, robotics, computer vision, applied mathematics, or a related
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these disciplines. Read more about our vision here: https://www.sdu.dk/en/om-sdu/institutter-centre/fysik_kemi_og_farmaci/ominstituttet The Ph.D. candidate will be part of the Pharmacy section: https://www.sdu.dk/da
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intelligence, and research software engineering, and is interested in developing robust, transparent, and sustainable computational tools. Experience with first-principles electronic structure methods, machine
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robotic research platform and an automated ‘Device Doctor’ for perovskite solar cells. The goal is to combine high-throughput experimentation, machine learning, and advanced modeling to accelerate device
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of machine learning Distributed and federated training The candidate is expected to hold a relevant MSc degree in Computer Science, Data Science, Physics, (Applied) Mathematics, Computational Statistics
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sustainable machine learning approaches and addressing renewable energy related projects. Likewise, deploying a novel paradigm of KGML (knowledge guided machine learning) can propel further research. PhD
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Job Description A PhD position in computational pharmaceutical science is available at the Department of Physics, Chemistry and Pharmacy (FKF) at the University of Southern Denmark (SDU). The
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groups in light–matter interactions and nanophotonics, as well as to theoretical physicists in the Department of Mathematics and Computer Science. We collaborate closely with scientists of the ALPS II
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with Robotics Excellent programmer in Java / C / Python / ROS or equivalent Excellent at using Machine Learning software, e.g. PyTorch / TensorFlow / Scikit Learn Highly knowledgeable in mathematical
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. You will also spend 3 months at Georgia Tech/Emory University (USA), working on machine learning and data benchmarking. Work description The selected PhD student will be responsible for the full