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and developing a machine perfusion system to improve organ preservation. The candidate will work in a dynamic, multidisciplinary environment alongside PhD-level engineers and scientists, graduate
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and inclusiveness. This is why we warmly encourage qualified candidates from all backgrounds to join our community. The School of Electrical Engineering is one of the six schools of Aalto University
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details of the research group are available at https://jmhutson.webspace.durham.ac.uk and a description of recent research achievements and current projects is available in a link from there. The successful
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
department in 1964. We believe that computer science’s greatest purpose is in working with other scientists to solve problems in their own disciplines. Therefore, we actively seek opportunities to generate new
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Research Center for Molecular Medicine (CeMM), ÖAW | Vienna, Virginia | United States | about 2 months ago
immune memory (Krausgruber 2020 Nature ), for high-content CRISPR screening (Bock 2022 Nature Reviews ), and CAR T cell engineering (Datlinger 2025 Nature ). As part of a newly funded ERC Advanced Grant
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Nutrition Postdoc Appointment Term: Fixed term for one (1) year with opportunity for renewal Appointment Start Date: ASAP Group or Departmental Website: https://colmanlab.stanford.edu/(link is external) How
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/2026 Group or Departmental Website: https://med.stanford.edu/supekar-lab.html(link is external) How to Submit Application Materials: Please email application materials as a single PDF to supekarlab
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synthesis out of fossil-based and one time use to continuous reuse of polymeric products. Current recycling methods cannot recover high-value polymers from complex waste streams such as multilayer packaging
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of future wireless communication systems. Finally, due to the large-scale nature, complexity, and heterogeneity of 6G networks, we use tools such as artificial intelligence/machine learning, quantum computing
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models for the fire resistance of LSF walls, including the development of machine learning models, experimental testing, and numerical simulations, within the project “FireLSF – Development of Predictive