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Field
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Qualifications • Highly recommended by scientists in the candidate’s field of study • Expertise in one of the following areas: Environmental or Performance Physiology, Machine Learning, Motion Analysis, Multiscale
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biology skills Experience with single-cell RNA-seq analysis Experience with machine learning based methods Have evidence of scientific accomplishment via peer-reviewed publications Understanding of cancer
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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific vision
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humanities disciplines. Familiarity with contemporary AI systems (e.g., machine learning, generative models) at a conceptual or applied level. Experience with qualitative or mixed research methods (e.g
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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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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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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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) 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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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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, and mentor graduate and undergraduate students, and may also teach one course per year for the Department of Statistics. A Ph.D. in Statistics, Biostatistics, Machine Learning, or a directly related