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Field
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master degree within preferentially reservoir engineering, or within applied mathematics, computational engineering, scientific machine learning, preferably acquired recently; or who possess corresponding
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these activities. Other laboratory responsibilities, as they arise, under the direction of the PI. Required Qualifications* MD or PhD degree in biomedical sciences (or related field) with a minimum of 3 years
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graphs (ARGs). Research areas include statistical/quantitative/population genetics, genealogical inference, machine learning, genetic prediction, genome-wide association studies, scalable linear mixed
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focuses on detecting underwater acoustics using AI methodologies. Additionally, CFD simulations combined with physics-informed machine learning will also be examined. Several research and industrial
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application of machine learning and AI methods to large-scale, longitudinal, routinely collected eRegistry data. The successful candidate will collaborate with researchers, PhD candidates, postdoctoral fellows
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learning approaches (Roux) to evaluate novel biomarkers for ADRD. Minimum Qualifications PhD in Computer Science, Engineering, Bioinformatics, Biomedical Data Science, or a related field Experience in
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. The objective is to further develop and validate machine-learning surrogate models derived from high-fidelity multiphysics simulations of reactor transients and quantify how surrogate uncertainties propagate
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the knowledge within a short time (e.g., 1 month). Have a degree in computer science, computer engineering, electrical engineering or equivalent. Possessing a Master’s or PhD degree will be advantageous
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. Ideal candidates will have demonstrably strong research skills, evidenced by multiple publications in top-tier machine learning or artificial intelligence conferences and/or leading scientific journals
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and