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The Argonne Leadership Computing Facility (ALCF) is dedicated to advancing scientific discoveries and engineering breakthroughs by providing world-class computing facilities in collaboration with
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under control or supervisory responsibility. 2. Recommends changes in research, testing or experimental procedures. 3. May be responsible for a single highly technical and complex piece of research
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or projects under control or supervisory responsibility. 2. Recommends changes in research, testing or experimental procedures. 3. May be responsible for a single highly technical and complex piece of research
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. The position is available for a fixed term of 36 months from time of appointment. This project is associated with a new EPSRC/UKRI-funded project entitled “Mastering charge-lattice interactions in novel
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with cutting-edge models and technologies—including patient-derived glioblastoma organoids, CRISPR-based screens, mass cytometry, and advanced microscopy—to dissect these complex biological processes
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 17 hours ago
, cultural, recreation and professional opportunities that span the campus and community. Primary Purpose of Organizational Unit The Chemistry Department is one of the largest and most complex departments in
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processes. A demonstrated interest in data visualization and large-scale data analysis is highly desirable. The ideal candidate will have a keen interest in understanding complex biological systems
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and biology research. A chemistry background is required with synthetic expertise and experience in the following techniques is highly desirable: Synthesis and characterisation of metal complexes and
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the analysis of large and complex datasets; iv) excellent written and oral communication skills; and v) the ability to work independently as well as in collaboration with a multidisciplinary team. Proficiency
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systems (ITS). In particular, the successful candidate will conduct cutting-edge research in: Developing physics-informed neural networks (PINNs) for complex dynamical systems modeling and observer design