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(energy auditing, GHG accounting, resource recovery)*Strong publication record and excellent written/verbal communication skills*Experience in coding for high performance computing (e.g., university cluster
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the Rutgers High Performance Computing infrastructure. The results of the research will be submitted for publication in a scientific journal. Preferred Qualifications Equipment Utilized Physical Demands and
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, pollution control, sensors, catalysis, etc.). The institute has a wide range of high-performance equipment, including eleven ISO 9001-certified technical platforms, which enable in-depth physicochemical
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Application Deadline 18 Nov 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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, and microscopy/imaging. A qualified candidate will be capable of working independently to perform lab work in this area, but also manages, trains, and schedules hourly student workers and undergraduate
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago
functioning. The successful candidates will assist with data analysis and publication, with the ability to publish high-quality work on a regular and timely basis. The successful candidates will also guide
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energy into acoustic waves, which can then be used to generate electricity or manage heat at the microscale. The goal is to improve the performance, efficiency and scalability of these microscale systems
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frameworks and pipelines Familiarity with single-cell multi-omic data integration and network or pathway inference tools Experience working in high-performance computing or cloud environments Interest in
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Carnegie Mellon University is a private, global research university that stands among the world’s most renowned education institutions. With ground-breaking brain science, path-breaking performances
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interested in computational materials design and discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials