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Field
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learning, and data science, focusing on applications within the healthcare, education, and environment sectors. Designs generative AI techniques and algorithms for data integration and computational models
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. with research expertise as following: Oncology, Cell biology, or Immunology. Prior experience with genetically engineered mouse models and in vivo work. Strong molecular biology and biochemical
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of fungal pathogens. The successful candidate will make extensive use of a range of in vivo models of fungal infection and will have access to the wide range of transgenic tools and in vivo immunology
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the area of fundamental modelling with molecular dynamic and ice/substrate interface bonding analysis. The main aspects of the research fellow role include conducting icing experiments in the Cranfield Icing
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differences more constructively. Fellows contribute to creating pedagogical resources, designing tools for dialogue, and testing models that help make engagement more inclusive and meaningful while also working
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frameworks. Expertise in modeling geophysical systems. Strong proficiency in machine learning libraries such as PyTorch. Proficiency in writing clean, efficient, and well-documented code. Knowledge
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snow in local and regional climate models is poorly constrained, leading to uncertainties in estimating mass loss through sublimation and snow redistribution. The PhD candidate will develop and execute
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the form of publications and at research seminars. Supervise students on research related work and provide guidance to PhD students where appropriate to the discipline. Contribute to developing new models
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model systems and functional studies to understand this process. Our goal is to develop new, targeted treatment strategies for men with advanced prostate cancer. The position is in a collaborative
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role will focus on nanophotonics theory and modelling. Key Responsibilities: The Research Fellow will work on a project to conduct the research on development of nanophotonic structures for quantum and