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Field
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Overview of the Role Are you experienced in machine learning and looking to apply your skills to solve new challenges and reduce disaster risk? Do you want to further your career in one of the UKs
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Engineering, or related field. Research experience with Artificial Intelligence/Machine Learning/Large Language Model. Publication track record in a series of top tier conference papers e..g, in NeuRIPS, ICLR
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for multivariate analysis and machine learning, ensuring high-quality metadata, traceability and reproducibility. Building on this data foundation, the Fellow will develop hybrid modelling tools that integrate
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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Date: August 15, 2026 (negotiable) Specialty Areas: Computational Linguistics, Language Acquisition, Cognitive Modeling, Machine Learning The Department of Linguistics at the University of Michigan
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., atomate2, AFLOW). Extensive knowledge of graph-based machine learning models for interatomic potentials, along with experience in generative models for the inverse design of inorganic materials. Proficiency
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scientific leaders and researchers. Job responsibilities The project aims to advance the use of machine learning techniques to model and understand plasma turbulence in magnetically confined fusion plasmas
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in empirical analysis using econometric, machine-learning, and language-modeling techniques. Conducting literature reviews and synthesizing existing academic research to support ongoing projects
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and validation of ML-based predictive models using multimodal data (neuroimaging, clinical, biomarker, and demographic). Design and implement computational pipelines, statistical and machine learning
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models, focusing on industrial image analysis Develop advanced deep learning methods for power battery inspection models Design and implement novel algorithms for AI-based CT imaging Lead experimentation