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Field
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or junior graduate students. A formal training, education, or certification in a secondary area (beyond the main PhD focus) such as additive manufacturing, advanced/hybrid manufacturing, machine learning
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publishing work as lead author. Experience with machine learning methods for modelling human learning, such as knowledge tracing and/or experience with conducting research that involves prompting or fine
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related discipline): Computer Science, Artificial Intelligence, or Machine Learning Economics or Econometrics (particularly applied micro, behavioural, or decision-focused modelling) Applied Mathematics
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) with questions related to this position. Major Duties/Responsibilities: Develop and apply machine learning models (ML) as surrogates for high-resolution process-based hydrologic models. Design and
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related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with transformer architectures (e.g., ViT
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track record in one or more of the following fields: (1) human-computer interaction, collaborative AI, (2) Generative AI/machine learning, (3) interaction design, experimental design, or evaluation
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on developing machine learning frameworks that enable robots to navigate complex human environments, and build longitudinal relationships which require personalization. Core Responsibilities: Modeling of social
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turbines represented using an actuator-line approach, assess the applicability and limitations of reduced-order models in predicting turbine performance, and develop machine-learning surrogate models capable
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, Higgs physics, particle dynamics in the early Universe, collider phenomenology, applications of machine learning to particle phenomenology, and lattice QCD, both within the Standard Model and beyond
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complex designs, and conducting big data analyses using advanced statistical models. Experience in the use of various datasets, e.g., TOPMed, UK Biobank, All of Us, and/or digital health data, is also