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Field
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using radiomic imaging algorithm in Hypoxic Ischaemic Encephalopathy). The Sophie project aims to develop a Neonatal MRI Biomarker for newborns with Hypoxic Ischaemic encephalopathy that is predictive
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national and international reference point in quantum technology research. The integrated approach—which combines experimental development, numerical simulations, and advanced algorithm design—will create a
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associate position to conduct research in machine learning (ML) for applications in High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including
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new opening for a postdoctoral scholar to develop cutting-edge mathematics and algorithms to analyze complex data from Department of Energy (DOE) experimental facilities. This role involves research and
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and tomographic radar capabilities. Our team is responsible for the algorithms which derive the biomass data product. The post-doc project is about extending the biomass algorithm to also include data
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systems that are both scalable and explainable. This role bridges algorithmic research and systems implementation, offering opportunities to collaborate with leading academics and engineers on developing
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learning based optimization algorithms, human and AI coordination in best decision making for urban transportation related problems. The role will focus on developing generic frameworks and innovative
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algorithms that significantly reduce computation cost, memory footprint, and power consumption. Design and optimize efficient training and inference pipelines for foundation models (LLM, VLM, VLA) across
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algorithms for dynamic master selection, coordinating BESS, PV, diesel generators, and other sources. Implement predictive, rule-based, or optimisation-based control strategies using MATLAB/Simulink, Python
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | 3 days ago
the unique nature of clinical medical data with low disease prevalence and difficulty obtaining ground truth data. Major regulatory science gaps include lack of methods for AI algorithm training with limited