467 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" scholarships
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attending an academic Bachelor’s degree in the scientific field mentioned above. Knowledge or experience (preferred) on machine learning or computer vision techniques, and interest in developing such skills
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-resolution (SR) technologies influence human and machine-based facial identification. The PhD will combine behavioural experiments, machine learning, and explainable-AI methods to answer questions: 1. Do SR
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Degree Dr rer nat Course location Münster Teaching language English Languages English only Programme duration 6 semesters Beginning Only for doctoral programmes: any time Application deadline https
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yield new insights into food-effector systems, sophisticated and tailored computational methods are needed. This project aims at combining probabilistic machine learning methods with prior knowledge in
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. These methods will integrate machine learning techniques and real-time sensor data, to enhance operational efficiency, reduce costs, and ensure desired service levels, such as meeting a high percentage of demand
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Science, Telecommunications, Applied Mathematics, or related fields; Solid background in probabilistic modeling, Bayesian inference, information theory, and/or machine learning; Experience with signal processing or decision
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solutions across the natural sciences. Your workplace You will be employed at the Department of Mathematics in the Division of Applied Mathematics, https://liu.se/en/organisation/liu/mai/tima . The research
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both independently and as part of an international, interdisciplinary team Assets•Experience with computer vision or deep learning (e.g. PyTorch, TensorFlow)•Familiarity with street view imagery or other
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for part-time employment. Starting date: 14.01.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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machine learning (ML) along with data from previously solved problem instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In