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Field
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The role The Atmospheric Chemistry Research Group (ACRG) and School of Engineering Mathematics at the University of Bristol have developed GATES, a graph neural network (GNN) machine learning model
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control, numerical analysis and scientific computing risk-averse and fractional models, digital twins and data-assimilating models, machine learning and neural networks (including operator learning and
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of at least some of the following: – Extensive independent research experience – Creativity and independence – Experience analyzing hyperspectral data and developing machine learning models - Genetic
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of complex systems, networks, and large-scale data Machine learning, generative AI, NLP, or algorithmic decision systems Ideal applicants will have a strong background in operations research, statistics
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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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analyzed with various Machine Learning and Data Science techniques to assess the dynamics involving case processing and costs. The first phase of the research will organize general data from the lawsuits
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focuses on developing cutting-edge statistical/machine learning methods for fitting complex, multi-institutional network models to partially observed hospital infection data. This research will directly
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strong command of data wrangling, cleaning, and large-scale dataset management. Machine Learning/Deep Learning: Experience with PyTorch, TensorFlow, or Hugging Face; embedding models; and model validation
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or similar statistical programming languages Prior experience with machine learning and natural language processing is a plus. Application Instructions: To be considered, please submit the following items
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Responsibilities: To conduct independent research in Multimodal Large Language Models and Federation Learning. To produce publications in top conferences and journals. To offer guidance and assistance to any