14 condition-monitoring-machine-learning Postdoctoral positions at University of Cambridge
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the University of Cambridge, UK. The Research Assistant will work together with a team of students and research collaborators on the development of learning-based control policies that facilitate the coordination
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, at the University of Cambridge, UK. The Research Assistant will work together with a team of students and research collaborators on the development of learning-based control policies that facilitate
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. Familiarity with standard design verification (DV) procedures and continuous integration (CI) setups would be beneficial. Knowledge of machine learning workloads and the design of machine-learning accelerators
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have experience in code development and/or use of first principles methods (e.g. DFT) and/or machine learning methods, as well as experience in working with experiments and/or experimental collaborators
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interactions in the condensed phase and at surfaces, with a particular emphasis on the development and application of first principles and/or machine learning approaches. Research in the Michaelides group
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machine learning tools and working on Linux High-Performance Computing platforms would be highly desirable. This is a highly collaborative role and you will work with scientists and clinicians from other
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modelling to study the causes and consequences of extreme chromosomal instability in these cancers. The role will involve: - Learning and applying cytogenetic methods for generation and analysis of chromosome
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lab members in advanced analysis techniques. The ideal candidate will have a strong research background in AI-driven human motion analysis, wearable robotics, and human-machine interaction. They should
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desirable. - Strong analytical skills, proficiency in quantitative analysis of large datasets and working with pre-trained machine learning models is desirable but not essential Click the 'Apply' button below
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and Content Learning: Addressing Sociolinguistic Diversity in India's primary schools', a project that investigates the role of alternative multilingual assessment tools for underprivileged learners in