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Engineering, Biomedical Engineering (Medical Informatics), or related areas. Recipient category: Masters, enrolled in the course: Degree courses: enrolled in doctorate. Non-conferring degrees courses: enrolled
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the application of rock physics models, Bayesian inversion methods, and machine learning algorithms in the electromagnetic context. Qualifications and personal qualities: Applicants must hold a master’s degree (or
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disciplines, including human-robot interaction, robot learning, soft robotics, computer vision, and agricultural robotics. About the PhD project: We are looking for a highly motivated and talented PhD research
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with an interdisciplinary team of statisticians, physicians, computer scientists, and health policy researchers. The successful candidate will lead development of variable importance measures – including
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individual to join this exciting collaborative research program that builds upon existing strengths in ADRD biology and genetics (JAX) and bioinformatics methods and machine learning approaches (Roux
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Department of Computer Science of Faculty of Science invites applications for a DOCTORAL RESEARCHER IN MACHINE-LEARNING, STATISTICS AND DATA-CENTRIC ENGINEERING starting from November 2025, or as
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development. Familiarity with software development lifecycle including design and documentation of software architecture, testing/debugging skills, and version control. Experience applying machine learning
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candidates will have a PhD or equivalent in a relevant discipline and experience in the development of machine/deep learning (ML/DL) methods for engineering and will use this experience in collaboration with
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from backgrounds, including computational chemistry, bioinformatics, systems biology, physics and machine learning. The project offers a unique opportunity to collaborate closely with experimental
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novel research methodologies in computer vision, deep learning architectures, and neuro-fuzzy systems to contribute to the development of robust AI frameworks for medical diagnosis and treatment support