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teams from universities, research institutions, and museums in a highly collaborative network, supported by the Muoniverse Research School, which coordinates training, exchanges, and career development
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generated quickly and regularly. Help develop machine learning techniques for feral swine abundance in data sparse environments. Collaborate with APHIS Wildlife Services (WS) to integrate data and model
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collaboration between Cranfield University, LIPTON Teas and Infusions, Browns East Africa Plantations and the UK Tea and Infusion Association. The project seeks to improve the sustainability of tea production
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management decision recommendations. You will collaborate directly with scientific staff to implement experiments and evaluate stress detection and decision support recommendation accuracy. Field data
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role in designing composites materials using inorganic solid electrolytes using computational modelling and machine learning. Qualifications • Ph.D. in Materials Science, Chemistry, Physics, or a
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strong sense of community & inclusion Enjoy a career that makes a difference by collaborating & learning from the best At UNSW, we pride ourselves on being a workplace where the best people come to do
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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, and interdisciplinary collaborations examining addiction mechanisms, eating behavior, and the design of the modern food environment. This position offers opportunities to contribute to high-impact
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to teach a seminar related to their research or teaching interests during the first year, and a lecture course during their the second year?each chosen in consultation with their faculty mentors and the
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. To advance the mission and serve their purpose, the USDA Forest Service balances the short and long-term needs of people and nature by: working in collaboration with communities and our partners; providing