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are seeking a Research Scientist specializing in Machine Learning, Data Science, Data Harmonization, and Synthetic Data Generation to lead the integration, standardization, and privacy-preserving algorithmic
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. In this PhD project, you will investigate foundation models for automotive imaging radar. The goal is to learn general radar representations from largely unlabelled data that can generalize across
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assistants, and large language models. Areas of interest for possible collaborations include but are not limited to: topological data analysis and topological machine learning; AI-assisted theorem proving
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artificial intelligence for science, knowledge-guided machine learning, and scalable scientific AI workflows. The successful candidate will conduct research at the intersection of machine learning, high
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the university/college community, and apply for jobs. Minimum Qualifications PhD in human-computer interaction, information science, learning sciences, computer science, or a related field, conferred by the start
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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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modelling of electrochemical systems, together with experience in machine learning and data-driven modelling. Research Associate: Hold a PhD in Aerospace Engineering, Chemistry, or a related discipline, or
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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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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