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Field
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explores novel aggregation methods at the intersection of AI safety, computational social choice, and judgment aggregation, aiming to formally integrate multi-stakeholder preferences into AI system design
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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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and brain that explain vision loss, building on our previously-developed method linking clinical, neural and behavioral data (Allen et al., 2018; Miller et al., 2019; Pedersini et al., 2023). We combine
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productively with both the CAISA research team and external organisations and institutions experience with mixed methods Furthermore, it will be considered an advantage if applicants can document: experience
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of parallel synthesis and purification methods is required. Some experience in molecular modeling and/or protein-inhibitor visualization is a plus. The incumbent must be able to synthesize, purify, crystallize
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. The successful candidate will contribute to further advancement of the experimental setup and utilize electron- and light-based advanced methods to conduct materials and surface science research. Duration: 12
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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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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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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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, 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