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/Qualifications Requirements: • Master’s Degree in Computer Science or Mathematics or Physics or a relevant discipline. • Background in machine learning or programming
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with health/real grid data or machine learning is beneficial but not mandatory. We Offer One year funded of 3-year PhD position (SIF Doctoral Student Grant). Access to high-performance computing
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related field with a strong quantitative focus. Strong programming skills in Python and demonstrated experience with machine deep learning frameworks (for instance, PyTorch or TensorFlow), preferably
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discipline. Desirable Experience in machine learning, deep learning, data analysis, numerical modelling, or scientific programming (such as Python, MATLAB, or R) is desirable. Knowledge of hydrodynamic
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machine learning. The project combines methodological research in statistics with applications to large-scale social science data. The successful candidates will have the opportunity to collaborate closely
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backgrounds such as AI, computer vision, computer graphics, machine learning, robotics, wearable technologies, textile engineering, fashion technology, digital fashion, or related areas are encouraged to apply
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schools, where children and adolescent research participants will complete computer-based learning tasks designed to capture metacognitive monitoring and regulation during task performance. The successful
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goal of your PhD project will be to develop combined X-ray and visible-light imaging technologies and machine learning methods for high-throughput inspection tasks. The work will include the development
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BMS constraints. Experience with system identification, uncertainty-aware modelling, large datasets, and machine learning. Evidence of research capability through a thesis, publications, conference
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demonstrable experience with programming in Python and implementing statistical or machine learning algorithms. You have experience with software development practices such as testing and version control with