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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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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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. 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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Center for Biologics Evaluation and Research (CBER) | Silver Spring, Maryland | United States | about 20 hours ago
will generate critical safety and efficacy data to inform regulatory evaluation of these emerging therapeutic modalities. Learning Objectives: If selected, you will have the opportunity to: Develop
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the research group. The post offers opportunities to learn and be trained in molecular biology techniques alongside their synthetic chemistry work. You will also help supervise and mentor MSc and PhD students
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the research group. The post offers opportunities to learn and be trained in molecular biology techniques alongside their synthetic chemistry work. You will also help supervise and mentor MSc and PhD students
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small molecules, as well as exciting new modalities (including ADCs and Immunotherapies). The key learning objectives of the fellowship program are to understand: • Scientific research process (including
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About Us The School of Science and Technology at NTU is an exciting multidisciplinary environment for learning, teaching and research, with some of the best facilities in the UK. We pride ourselves
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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