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Science. Commitment to undergraduate and graduate education. Demonstrated expertise in machine learning/deep learning and software development (Python; PyTorch/TensorFlow). Peer-reviewed publications and strong
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of degree equivalency. Preferred Qualifications: Background in medical imaging, imaging simulation, and machine learning. Programming in Python, MATLAB, C, CUDA. Other Requirements: This position is hybrid
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complementary and synergic methods at the intersection of Artificial intelligence, Machine learning, Numerical simulation, Formal verification. Such methods include, among the others: AI-guided simulation
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CDT in Machine Learning Systems About the CDT Machine Learning has a dramatic impact on our daily lives built on the back of improved computer systems. Systems research and ML research are symbiotic
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imaging using deep learning. You will study the water imbibition in hierarchically porous Si‑based material systems across multiple length and time scales. These systems can manipulate fluid transport
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PhD studentship: Adaptive Logic-Based Machine Learning Grids Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate). Additional project costs
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Learning, Natural Language Processing, Human-Computer Interaction, Digital Health, Endocrinology Secondments (Preliminary Plan): UiB (Norway): 1–2 months — Patient and caregiver interviews, exploration
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, Data Science, Machine Learning, or a related field. Experience and skills · Strong knowledge of AI, Machine Learning, data-science (e.g., neural networks, deep learning, autoencoders, GANs, active
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University Medical Center Utrecht (UMC Utrecht); Published 31 Oct ’25 | Netherlands | about 2 months ago
validating a machine learning prediction model for CP and related outcomes using neonatal MRI, EEG, GMA, HINE, and clinical data. Coordinating patient inclusion, data collection, and analysis across multiple
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; multiple courses to follow from our Teaching and Learning Centre; multiple courses on topics such as leadership for academic staff; multiple courses on topics such as time management, handling stress and an