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Center for Biologics Evaluation and Research (CBER) | Southern Md Facility, Maryland | United States | about 13 hours ago
communications; Become an active part of a dynamic, diverse and results-oriented team; Gain a deeper understanding of appropriate models and endpoints that can be used assess therapeutic potential of antibody
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to better understand and characterize variability of water at the land surface - i.e. in soils, snow and groundwater - to help in predictions of future water availability, global water cycle dynamics and sea
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of dynamic physical systems and control system algorithms experience in software development, data analysis and AI practical experience in experimental work motivation and potential for research within
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Associate/Fellow in Regional Studies with qualitative / ethnographic research skills to join a dynamic research team exploring “Regional decarbonisation transitions: an inclusive, place-based approach
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many disciplines. A space where the brightest minds can pursue big and bold ideas and discover answers to crucial scientific questions. We support them in a dynamic environment which fosters excellence
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. Generous personal development opportunities and mentorship in an established research group Opportunity to work with faculty in mentoring graduate and undergraduate students. Exposure to the most dynamic and
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Excellent communication and scientific writing skills in English are required Additional Information Benefits We offer Our institution is based on an interdisciplinary, innovative and dynamic collaboration
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an agile, dynamic, and innovative university committed to making a positive impact through our excellence in education, research and innovation and the contributions we make to the wider community. We
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dynamical evolution models (N-body) to investigate the occurrences of subsequent collisions, using a surrogate model for giant impacts trained on SPH simulations, and then to analyse the results in
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substantial complexity. This dynamic interplay makes the multi-reservoir system both ideal and challenging for developing advanced spatiotemporal forecasting models. By integrating causal inference with