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algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
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regression models using Stata or R. Collecting, managing, and structuring quantitative datasets Conceptualization of suitable empirical methodologies and models Statistical analyses of complex datasets and
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have the following responsibilities: Development and implementation of hardware and software; Data analysis and modelling; Coordination of field measurement and data analysis; Writing reports and
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, in particular, collecting and analysing AI-assisted human choices and deliberation data. You will be excited to explore the opportunities and risks of large language models for AI assistance of human
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learning, and data science, focusing on applications within the healthcare, education, and environment sectors. Designs generative AI techniques and algorithms for data integration and computational models
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Responsibilities Development of new machine learning modeling approaches Development of new advanced control and optimization algorithms Optimization of carbon capture process operation Provide regular project
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and identification of pathology specific biological targets for treatment options. Finally, we will integrate our engine exhaust exposure models as an exemplar IPF driver to understand mechanisms
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and modeling of properties and spectra of PFAS chemicals with the goals of improving detection of PFAS compounds, replacing PFAS in plasma etching processes, or identifying solid adsorbent additives
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Assurance and verification offerings for AI are fragmented and target specific areas of AI lifecycle (e.g. model assurance or prompt injections) without offering a unified view of the security
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pharmacology and modeling strategies and building clinical pharmacology and pharmacometrics models to support dose selection at various stages of drug development. The Fellow will support the Clinical