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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 22 days ago
data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological
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regularly publish at top ACM conferences and HCI journals, in interdisciplinary venues such as the Big Data & Society journal as well as across venues in the humanities and social sciences. Terms
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and experimentation in the area of Multi-agent Agentic AI systems applied to 6G network and service management. By leveraging recent advances in Large Language Models (LLM) and other key agentic tools
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
Experience * Proficiency in analyzing functional magnetic resonance imaging data. * Demonstrated skill in manuscript preparation, managing large datasets, multivariate analytical approaches, and/or
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data collection activities, ability and interest in working with large datasets, and interest in working with interdisciplinary teams of researchers. The expectation is that the scholar would help lead
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The Leibniz Institute for the Analysis of Biodiversity Change (LIB) is one of the large, globally connected research museums of the Leibniz Association. In addition to excellent research
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assays such as light/dark, von Frey, and facial grimace. Additionally, the successful candidate must possess technical proficiency in MATLAB and Python for data analysis and processing Duties to include
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biology and genomics. We are collaborative and multidisciplinary, combining organic chemistry, cell and molecular biology, protein biochemistry, and large-scale genetic screening in our research approach
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research experiments within the Pediatric Brain Tumor Research Program. This position will be responsible for evaluation and analyzing large sequencing and multiome data-sets. Baylor College of Medicine
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Science at Fred Hutch Recent advancements in statistics, computational techniques, and experimental technologies are transforming how we generate and analyze data. This big data has big potential