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Your Job: Develop AI pipelines that translate -omic signatures into dynamic model parameters Implement reinforcement-learning agents that optimise model performance Collaborate closely with
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performance, particularly during extreme events, addressing limitations inherent in traditional meteorological monitoring networks. Methods to be used: The research will focus on the integration of Transformer
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, which aims to revolutionize solvent-based capture by developing an AI tool to screen over 1 billion solvent formulations. Top candidates will be selected based on performance criteria such as absorption
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emerged as important predictors of response to immunotherapy. However, the molecular and spatial cues guiding their development remain poorly understood. Your work will include: • Performing spatial
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To employ a broad knowledge of principles, practices, and procedures in a particular field of specialization. To plan, coordinate, and conduct research. Perform survival surgical procedures included
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intuitive links between search parameters and the resulting geometry or performance outcomes. The result will be an LLM-driven framework giving designers clearer cause-effect control over generative shape
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of research proposals, co-supervision of researchers and PhD students, organization of events, and coordination of research spaces. Main Duties and Responsibilities Perform the following activities in
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cyclone-induced winds from synoptic wind conditions. Additionally, extreme value statistical methods will be applied to establish predictive risk models for tower performance under rare wind events, leading
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between search parameters and the resulting geometry or performance outcomes. The result will be an LLM-driven framework giving designers clearer cause-effect control over generative shape optimisation. 2
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are looking for a highly motivated and skilled PhD researcher to work on structural surrogates of offshore wind foundations through graph-based machine learning. Our goal is to perform full-structure