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application to medical imaging (e.g., MRI) · Experience with MRI data analysis, network science, graph theory, topological analysis, or related computational approaches, especially in Alzheimer’s
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postdoctoral community. PRISM opens the door for you to: Identify potential mentors and clarify your goals for postdoctoral training Visit campus and explore the research and living environment Get access to top
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. This position offers an exciting opportunity to work at the intersection of HPC and AI, addressing critical communication bottlenecks and optimizing network interconnects for large-scale distributed systems
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/neuropixel probes and electrical microstimulation to study attention and decision making networks in a behaving animal model together with parallel studies in humans. The project is part of a NIMH Silvio O
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large language model engineering Experience producing quality research outputs Strong skills in communication, collaboration, and problem-solving Desirable skills include: Expertise in LLM recommendation
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research on a project to harmonize phenological data from sources such as the National Phenological Network, herbaria, and the National Ecological Observatory Network and perform analyses to understand
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to support community-based organizations, funders, and other stakeholders working towards social, racial, economic, and environmental justice. ERI produces data-driven analysis and rigorous research to inform
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experimentation on optical communications in metro and access networks in support of next generation of mobile services. The position will be based in the CONNECT research centre, in The School of Computer Science
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are particularly welcome from women, people from the Black, Asian and Minority Ethnic (BAME) community, and other protected characteristics who are under-represented in research posts at the University. Equality
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | about 2 months ago
, mathematics, and data science. Collaborative projects merge traditional geographic research with advanced computational methods such as graph neural networks (GNNs) and large language models (LLMs) to explore