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Field
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behavior of pests, their response to pesticides, including physiological and genetic mechanisms of resistance, innovations in applied pest management research (e.g., molecular, computational modeling
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The doctoral student project and the duties of the doctoral student This Data Driven Life Sciences (DDLS) PhD project focuses on probabilistic models of protein structure, which can be used primarily
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are encouraged to apply. As DTU works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be conducted on qualified candidates
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nanoparticles in drug delivery or diagnostics, familiarity with cellular targeting, and use of computational tools to model nanoparticle behaviour. Experience in a regulated research environment is also
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are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
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mechanisms and transformer models. Qualifications Bachelor’s degree with First Class Honours (or equivalent) and/or a research master’s degree in computer science, AI or Computer Vision; or an equivalent
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postdoctoral fellow interested in gaining trainingand experience in disease modelling and translational science. The successful candidatewill lead collaborative efforts among basic and clinical researchers and
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to these challenges, working with high performance and distributed computing environments, working with large-scale machine learning models, and a proven research record of scholarly contributions through publications
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nationals. As this position is sponsored by the MTC, any successful candidate would need to pass the sponsors own security checks prior to the commencement of the PhD. Motivation Automation is key to meeting
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qualification is required at time of hire. Required Qualifications Completed Ph.D. in Ecosystem or Landscape Ecology (or related field such as ecosystem modeling, biogeochemistry, or climate change ecology