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. Significant experience of developing deep learning methods using computational frameworks such as PyTorch, TensorFlow etc. Experience of working with molecular questions in the biosciences An interest and
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., NeurIPS, ICML, ACL, EMNLP, etc.). Proficiency in programming languages such as Python, and experience with deep learning frameworks like TensorFlow, PyTorch, or JAX. In-depth understanding of transformer
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mathematically rigorous approaches to optimize the trade-off between privacy and utility especially in the context of large models. Advance knowledge of key AI methods such as deep learning, algorithm design
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description SIT's mission is centred on nurturing industry-ready graduates who possess deep technical expertise and
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time at NY Creates Albany NanoTech Complex, where you will collaborate with staff using 193 nm deep-UV ltihography to fabricate devices at 300mm-wafer scale. Essential Duties and Responsibilities: You
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Université Grenoble Alpes, laboratoire TIMC, équipe GMCAO | Grenoble, Rhone Alpes | France | 3 months ago
with expertise in medical image processing—particularly registration and segmentation—and proven experience in deep learning, with a focus on ultrasound imaging. Prostate cancer diagnosis relies
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of machine learning for healthcare and related topics Deep knowledge of multi-modal learning, transfer learning, foundation models, and self-supervised learning. Experience in dealing with large
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. Familiarity with machine learning (ML) techniques for optimizing models or analyzing complex datasets. Experience with space-based instrumentation or similar high-precision systems. Demonstrated expertise in
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methodology, theory, and applications across the areas of Bayesian experimental design, active learning, probabilistic deep learning, and related topics. The £1.23M project is funded by the UKRI Horizon
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: Verifiable world models. The research will focus on developing a new class of structured, verifiable world models that integrate the flexibility of deep learning with the rigor of formal methods and