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models; 2. Statistical methods, analysis, and inference for large-scale computational simulator applications; 3. Uncertainty representation, quantification and propagation; and 4. Scalable data science
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other sources to train and validate AI models. Develop computational workflows incorporating LLMs, Monte Carlo Tree Search (MCTS), phylogenetic inference, uncertainty quantification, and epidemiological
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-of-the-art data management, machine learning and statistics techniques. With the advancement of Exascale systems and the variety of novel AI hardware designed to accelerate both training and inference
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/computing and data analytics, track records of publications in peer-reviewed journals, a strong problem-solving capability, a highly skilled implementation capability, and strong interest in solving real
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quantitative data analysis. Excellent communication and teamwork capabilities, with a track record (or potential) in writing publications and presenting scientific results. Interpersonal skills, oral and written
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++, or similar, with experience in data-driven workflows and computer vision Demonstrated track record of peer-reviewed publications Highly collaborative, innovative, and capable of working independently in a
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strong track record in problem solving and scientific publications. The candidate will be expected to conceive of, plan, and implement the scientific research, and to report relevant results in
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electrochemical characterization of designer interfaces. Position Requirements The successful candidate will be highly motivated and have a strong track record in problem solving and scientific publications
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as evidenced by publications and recommendations. Proven track record showing the ability to carry out independent and collaborative research in a multidisciplinary team while meeting project
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implementing the scientific research, and for reporting relevant results in an appropriate form. The successful candidate should be highly motivated for research excellence and have a strong track record in