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the School of Computer Science at the University of Sheffield, you will lead the machine learning research on acoustic foundation models for respiratory health. You will work with large-scale real-world
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predictions of river flow direction and connectivity. You will lead the development of scalable and reproducible data and machine-learning pipelines, upgrade the GRIT global hydrography, and design and evaluate
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systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization
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PhD candidate will have: A strong background in computer science, artificial intelligence, machine learning, or a closely related field. A solid understanding of machine learning concepts, particularly
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, computational biology, statistics or a closely related field. You have strong programming skills, preferably in Python, and experience with machine learning or deep learning. Experience in computer vision
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Are you a computer scientist or machine-learning researcher interested in making medical-imaging AI work reliably beyond the dataset on which it was developed? We have an exciting two-year Grade 7
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programming skills in Python. Strong foundation in statistical genetics and quantitative methods for analysing large-scale genetic and molecular data, with familiarity with modern machine learning approaches
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is developing research excellence in areas including Machine Learning, Big Data, Visual Analytics, Computational Intelligence, Machine Learning, Software Implementation and Testing, and their
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biological domains is a must PhD in computational biology, computer engineering, computer science, (bio)statistics, artificial intelligence, physics, or related. Desire to push the frontier
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fusion, machine learning, and systems modelling. We are at the forefront of method development towards large-scale data analysis and modeling of biological systems. Together with a wide range of