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Field
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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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research team to contribute to projects at the intersection of AI/ML, real-world data, and health outcomes. The Postdoctoral Researcher will play a key role in developing and applying cutting-edge methods
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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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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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Analyze and assess risks of de-anonymization, including researching current de-anonymization strategies Investigate synthetic data generation methods and their utility Deploying and benchmarking the above
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/MS for example) Advanced knowledge in research design and methods (running extensive experimental studies) Familiarity with practices of transparency and open science Strong interpersonal and
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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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disparities for communities and populations. Candidates will receive mentorship and training in precision health, including advanced statistical methods focused on predictive modeling in relation to response
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/MS for example) Advanced knowledge in research design and methods (running extensive experimental studies) Familiarity with practices of transparency and open science Strong interpersonal and
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methods, and contribute to high-impact publications. Responsibilities also include presenting research at national and international meetings, with ample opportunities and mentorship for career development