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characterising chronic diseases and disease patterns from electronic health record (EHR) data through the development of advanced deep learning methodologies based on state-of-the-art foundation models. You will
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; • Integration of Genomic Prediction and Crop Growth Models; • Genomic prediction & GWAS optimization; • Publication of scholarly works; • Occasional grant writing; • Occasional graduate student mentoring
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, we focus on three major areas of research: 1) high throughput screening of bacterial metabolites and genes to identify effector functions, 2) mouse models of inflammatory and metabolic diseases, and 3
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of the Autism Diagnostic Observation Schedule-2 (ADOS-2). Experience performing advanced statistical modeling and analysis with longitudinal and multivariate datasets, including multi-level modeling (MLM
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Requisition Id 16569 Overview: The Grid Transients & Advanced Modeling Group in the Electrification and Energy Infrastructure Division (EEID) within the Energy Science and Technology Directorate
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such as quantum and analog computational models. You will explore how compilers, runtimes, and AI-driven agents can co-optimize complex architectures, reasoning across conventional processors (CPUs/GPUs
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partners. In this role you will: Design, build and flight-test several (scaled) models of the medical delivery drone(s) De-risk the flight controller of the eVTOL aircraft Optimise the full-scale medical
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Flexibility to contribute across multiple projects and research applications Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork Preferred Background Experience in one
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applications. Research activities may involve geophysical forward modeling, AI-driven geophysical inversion, seismic monitoring and imaging, scientific machine learning, depending on the specific research focus
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 months ago
/Description: The Institute is recruiting for a Post-Doctoral Research Associate position who has a strong background in systems-based approaches to modeling risks that involve consideration of hazard (e.g