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the knowledge within a short time (e.g., 1 month). Have a degree in computer science, computer engineering, electrical engineering or equivalent. Possessing a Master’s or PhD degree will be advantageous
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Successful candidates will have publications in information theory and machine learning venues, such as IEEE Transactions on Information Theory, ISIT, NeurIPS, ICML, ICLR, and ACM FAccT. Experience in machine
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computer vision and vision-language models Experience with ML evaluation metrics and benchmarking Proficiency in Python and deep learning frameworks (e.g., PyTorch) Interest in applied, industry
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strategic career path, all PhD fellows are expected to submit a career development plan, specifying career goals and the competencies that the PhD fellow should acquire, no later than one month after
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many areas of applied and theoretical statistics and data science, and is heavily involved in research at the crossing of statistics and machine learning. The focus of this postdoctoral fellowship is to
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Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi
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of statistics and machine learning. Modern vessels produce vast amounts of multivariate data streams. The project addresses the development of trustworthy statistical and machine learning methods for anomaly
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. transformer models). One focus of this work will be on B-cell receptor evolution. Experience in applications of modern machine learning methods as well as in biological data analysis are needed for the position
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: Knowledge of kinetic modeling and/or machine learning interatomic potentials. Background Investigation Statement: Prior to hiring, the final candidate(s) must successfully pass a pre-employment background
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should acquire, no later than one month after commencement of the fellowship period. The department is responsible for ensuring that the plan is followed up and that the PhD fellow has access to career