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Job Description We offer an exciting postdoc position dedicated to developing methods for detecting performance disparities in foundation models for fetal ultrasound and understanding what causes
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to develop innovative methods and actionable tools for detecting, analyzing, and preventing vulnerabilities in supply chain systems, leveraging state-of-the-art AI and ML techniques to improve overall security
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in Cambridge. The mission statement of the group is "developing statistical methods to use genetic variation to answer clinically important questions about disease aetiology and prevention". The three
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considered an asset Training and experience in quantitative empirical methods Good command of multivariate and complex statistical methods International publications in peer-reviewed journals and / or book
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perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems This position
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methods such as gas chromatography, Karl-Fischer (for water), titration methods for TAN/CAN, elemental analysis etc. Synthesis and characterization of catalysts. Writing of scientific papers and attending
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experience in machine learning methods, tools, and platforms. Proficiency in Python, with demonstrated software development experience. Hands-on experience in MLOps, including the design and deployment
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, Luxembourgish and / or French will be considered an asset Interest in empirical social research, excellent command of quantitative and / or qualitative empirical methods; openness to mixed methods research
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methods, including Mendelian randomisation, to identify candidate mechanisms linking circadian misalignment and sleep disturbances with cardiometabolic disease. The successful applicant will join an
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the following primary tasks: Develop inspection methods for assessing component condition and material quality Perform testing and analysis to support remanufacturing decisions, including Elemental composition