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computational tools to support the safe and ethical deployment of AI in clinical settings. The research focus is on AI performance monitoring, distribution shift detection, bias assessment, and stress testing
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. The candidate will work on wind turbine and wind farm control, developing innovative control algorithms and validating them through wind tunnel testing. The candidate will also be responsible for teaching
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About us: We are seeking experts in medical image deep learning to join our team and help develop novel computationally efficient segmentation algorithms. We welcome application from individual with
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-based prediction. This includes instrumenting a high-power APS torch with video and acoustic sensors, developing machine learning algorithms for feature extraction, and building predictive models
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algorithms from concept to implementation; and performing systems integration and testing for technology maturation. Minimum Qualifications: 1. Ph.D. in relevant technical field 2. 6 years experience in
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demonstrated ability to apply these techniques in the biomedical or health informatics domain. Strong algorithm development and programming skills are required, along with excellent teamwork and communication
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Research Assistant for Applied Mathematics - Responsibilities - Analyze a dataset of case law in the domain of worker classification and develop algorithms to predict judges decisions based
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modelling and simulation of transmission and distribution networks, including benchmarking data models, developing optimal power flow algorithms, and creating state estimation and multi-energy optimisation
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Matter Expertise – Work with a diverse group of experts, including engineers, scientists, and DoD personnel to understand problems from many perspectives Develop prototype algorithms – Design, write, and
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validate these distributed intelligence algorithms, enabling breakthroughs in scientific research across DOE domains. The candidate will collaborate with DOE’s SWARM project (https://swarm-workflows.org