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Field
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technologies with existing energy infrastructure Engage with policymakers, industrial partners and stakeholders to align project goals and secure off-take and PPA-related insights Pilot and test emerging clean
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, security enhancement, and vulnerability research of Generative AI. The fixed-term position starts based on mutual agreement, and ends on 31.8.2029, which is the estimated end date of the project. The project
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applicant will have: A PhD in Materials Science, Applied Physics, Electrical Engineering, Chemistry or a closely related discipline. Strong background in modeling and simulation of thermal/energy systems
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geophysical monitoring, data integration, and the development of predictive models and visualization tools. Candidates should have a PhD in geophysics, hydrology, environmental engineering, or a related
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instrumentation while strictly adhering to applicable safety standards. Analyzes, interprets, and disseminates research results through group meetings, scientific presentations, manuscripts, periodic reports, and
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projections under climate change and plays a key role in evaluating flood safety targets and risk-reduction measures. We are looking for a candidate to run the GFZ model chain RFM (Regional Flood Model
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time off. To access this tool and learn more about the total value of your benefits, please click on the following link: https://resources.uta.edu/hr/services/records/compensation-tools.php CBC
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sponsorship. Job Duties Performing high level experimental, theoretical, and numerical research and data analysis (55%) Presentation publication, and proposal preparation (20%) Mentoring PhD and MSME students
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coatings and additives to secure stable particles of these redox active materials. We’re looking for a postdoc with a background in electrochemistry, with an excellent performance for his/her career stage
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reliability of the AESEC experimental platform. Additional activities, where applicable: Participate in the scientific life of the team and in URANUS project meetings. Occasionally supervise or train PhD