1,047 machine-learning-"https:"-"https:"-"https:"-"https:"-"Ulster-University" Fellowship positions
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the devastating disease avian coccidiosis. The secondary goal is to compare various Eimeria spp. to identify genes involved in intestinal cell specificity, virulence, and markers of drug resistance. Learning
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. Through these experiences, it is anticipated that you will learn how to: Operate and develop custom aerosol generation equipment including software modifications for associated chambers. Operate
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Fellow will help mentor undergraduate students, work with Center for Health Research staff and will have the opportunity to teach classes by separate assignment as a part-time lecturer. This position will
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beyond the military. By providing a platform for distinguished researchers to contribute to the Air Force's science and technology initiatives, the AF STFP plays a crucial role in maintaining
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About the job: The Transforming Cultures of Language-Learning (TransCoLL) project, funded by Arts and Humanities Research Council, aims to intervene in the steep decline in Anglophone language
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in each crop area and learn basic agronomic, data collection, and plant breeding methodologies in trials and nurseries planted at the USDA-ARS. Learning Objectives: The project assignments will provide
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Computer Science, AI/ML, Computational Biology, Food Science with computational expertise, or a related field. Experience with natural language processing, machine learning frameworks (e.g., PyTorch
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nowcasting platform that delivers real-time, hyperlocal information on urban heat risks in tropical cities. Leveraging Doppler lidar–based microclimate studies and machine learning, the research emphasizes
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organs for transplantation from deceased organ donors (including machine perfusion of donor livers and normothermic regional perfusion). Fellows will also be mentored in the preoperative evaluation
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solvers, with the goal of exploiting models of various complexity, ranging from high-performance computing, via reduced-order models to data-driven (machine-learned) representations. In particular, we