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application to lineage tracing Algorithms for characterizing structural alterations in bulk and single cell whole-genome data Mutational signature analysis for cancer/brain samples Analysis of repetitive
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of Engineering and Applied Sciences. The fellow will design and run human experiments, perform data analysis, and create computational models of learning and memory. A PhD is required. An ideal candidate will be
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. Depending on available funding, one of the positions may be specifically targeted at candidates interested in the scaling regimes of deep networks. Basic Qualifications A PhD is required. We seek candidates
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approaches such as digital technology or task-sharing for scale-up of proven interventions. We welcome applications from recent PhD graduates who are interested in these or related fields, particularly those
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their PhD in economics, business, psychology, public policy, political science, environmental science, statistics, or related fields, and whose research interests include private sector initiatives aimed
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eligible. Information regarding postdoctoral fellow salary, which is determined by the number of years post PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines . With
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scholars who have recently completed their PhD in economics, business, psychology, public policy, political science, environmental science, statistics, or related fields, and whose research interests include
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scholars who have recently completed their PhD in economics, business, psychology, public policy, political science, environmental science, statistics, or related fields, and whose research interests include
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, covering areas such as LLMs, generative AI models, computational biology, and the analysis of high-dimensional neural and behavioral data. The Fellowship is intended for early-stage scientists and will
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the PhD no earlier than July 31, 2021. For candidates who will have completed the PhD within twelve months of the August 1, 2026 start date, verification of completion of the degree will be required prior