20 machine-learning "https:" "https:" "https:" "https:" "https:" positions at KINGS COLLEGE LONDON
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have an international profile in research and education in any of the following areas: systems security, security engineering, security testing, computer forensics, AI for security and privacy, and
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have: A PhD (or equivalent) in a relevant discipline (e.g., biostatistics, machine learning, computer science, clinical informatics, natural language processing). Strong skills in data analysis and
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Robotics to develop research in the field, e.g., robot design, control and mechatronics; Publication record in robotics and machine learning, e.g., ICRA, IROS, RSS, CoRL, T-RO, CVPR, ICML; Excellent verbal
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Details: informatics-hod@kcl.ac.uk Where to apply Website https://www.timeshighereducation.com/unijobs/listing/404894/reader-in-computer-… Requirements Additional Information Work Location(s) Number
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About us The Department of Informatics is looking to appoint a Reader in Computer Vision Education. This is an exciting time to join us as we continue to grow our department and realise our vision
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such as: Systems security, Security engineering, Security testing, Computer forensics, AI for security and privacy, Security and privacy of AI. Our department addresses computer science challenges from a
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forensics, AI for security and privacy, Security and privacy of AI. Our department addresses computer science challenges from a broad perspective. Faculty members regularly publish in and serve on the program
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large-scale speech and wearable data from participants in the GLAD Study cohort (https://gladstudy.org.uk/ ). Using large language models (LLMs) and acoustic analytics, they will uncover patterns in
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system may hold clues to how psychosis and other psychiatric disorders are caused and how people respond to treatments. We will investigate blood and cerebrospinal fluid from patients and use machine
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metabolism Strong problem-solving skills and the ability to develop novel computational methods for data integration and analysis Experience with machine learning approaches for biological data modeling and