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complex materials systems. Advanced data analysis and scientific model development using Python or other scientific programming languages, including experience with automation, instrumentation control
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, instrumentation control, or feedback systems. Demonstrated ability to design, fabricate, operate, troubleshoot, and repair laboratory equipment, including custom-built or high-temperature/high-pressure systems
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with salary development according to seniority in the position. From the salary, 2% is deducted as a contribution to the Norwegian Public Service Pension Fund. automatic membership in the Norwegian
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)hydrologic models, and produce novel scientific insights regarding hydrologic responses and their controls during climate extremes. Required Department Minimum Qualifications: Ph.D. or other terminal degree in
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of the grant: Jacinto Paulo Simões Estima; Catarina Helena Branco Simões da Silva IV - Work Plan / Goals to be achieved: Title: Automatic Container Recognition in Dry Ports Summary description
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on Smart Fibre-Optic High-Power Photonics (HiPPo). The HiPPo programme (https://www.hippo-laser.co.uk/ ) is focused on understanding how to control the properties of fibre lasers, to go beyond the “fixed
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earlier and empowering patients to take control of their lung health. You will work closely with an interdisciplinary team from six institutions: Professor Moi Hoon Yap (Manchester Metropolitan University
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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | 13 days ago
quantitative method support for guidance development, abbreviated new drug application (ANDA) reviews, citizen petitions, controlled correspondence, pre-ANDA meetings, methodology development for bioequivalence
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for completing the doctoral degree within the timeframe ability to work independently and in a team, be innovative and creative ability to work structured and handle a heavy workload having a good command of both
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tools and their integration into the matRad treatment planning system; 3) development of tools that automatically identify for each patient the dose-response models that characterize the worst-case