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-dimensional probability, concentration and functional inequalities ? Mathematical aspects of machine learning and deep neural networks ? Free Probability aspects of Quantum Information Theory. While excellent
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successful candidate will possess skills in natural language processing and deep learning. Experience of studying the robustness and generalisability of LLM would be beneficial. This is a full time post (35
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Electrical Engineering, Computer Science, or a related field Strong background in speech processing, signal processing or machine learning Proficiency in Python and deep learning frameworks Experience with far
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responsibilities Design, implement and benchmark deep machine learning models for large-scale cancer datasets that include genomics, transcriptomics, epigenomics and imaging data Collaborate closely with
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Electrical Engineering, Computer Science, or a related field Strong background in speech processing, signal processing or machine learning Proficiency in Python and deep learning frameworks Experience with far
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robust models – and for clinicians, whose goal is to determine when to trust the models. We therefore seek candidates who have strong technical background in working with large-scale deep learning models
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under construction at SURF. The expected start date for this position is as early as August 1, 2025. Qualifications: A PhD in high-energy physics or a related field is required. Experience with detector
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communication) Willingness to learn and confront new challenges Preferred Qualifications Doctoral research conducted in the area of machine learning for healthcare and related topics Deep knowledge of multi-modal
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foundational and applied topics in computer vision and machine learning, with particular strengths in inverse problems, generative models, and geometric deep learning. We work across diverse application areas
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the intersection of machine learning and genomics. The project involves the development and application of advanced machine learning and deep learning techniques to understand the sequence-function relationships