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on software engineering, computational infrastructure, and open-science support, working across multiple work packages. The postholder will play a central role in maintaining and improving the group’s
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Requisition Id 16063 Overview: The Centrifuge Science (CS) Section within the Enrichment Science and Engineering Division (ESED) at Oak Ridge National Laboratory (ORNL) is seeking an Electric Motor
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of results through high-quality publications and open-source software contributions Your Profile: Master’s degree in chemical engineering, biotechnology, computational biophysics, bioinformatics, data science
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
candidate will join a vibrant research environment on the UNC Chapel Hill campus, collaborating closely with scientists, engineers, technicians, and students at UNC and Triangle Universities Nuclear
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into open-source software tools and data portals of the institute • Collaborate with internal and external, as well as national and international, project partners Present your results at national and
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demonstrate their potential in a Europe-wide ecosystem reanalysis. The outcomes will include open-source software, scientific publications, and a PhD thesis. Your tasks within framework in detail: Conduct a
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updates. Minimum Requirements (Essential) Education PhD in Computer Science, Artificial Intelligence, Data Science, Computer Engineering, Biomedical Engineering, or a related field. Technical Skills
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software, and close integration with experimental and modeling groups across institutes and Helmholtz programs. Structured doctoral training and international visibility: The PhD candidate will be part of
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Students Faculties and centres Centre for Aboriginal Studies Faculty of Business and Law Faculty of Health Sciences Faculty of Humanities Faculty of Science & Engineering Science courses Engineering courses
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-source software, scientific publications, and a PhD thesis. Your tasks within framework in detail: Conduct a literature review on modern techniques for combining models with observational data, with a