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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month ago
Python or R is preferred. Ability to analyze multi-omic data, including microbiome data, would be an advantage. Ability to build algorithms and data pipelines would be ideal. Create a Job Match for Similar
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associate will conduct theoretical studies and numerical simulations, develop new methods and algorithms, and mentoring junior group members. Physical Demands and Working Conditions Physical Activities
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/or observationally motivated model-building for SMBH feeding and feedback. The group integrates observations, theory, data analysis, algorithm development, model-building, and numerical simulations
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meetings Potentially participate in Arctic field campaigns Be working with large data sets and developing algorithms. You should be highly motivated, self-driven, and possess strong work ethics, team spirit
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new algorithms to analyse compliance of CER according to relevant Australian standards (AS 4777.2020.2, CSIP-AUS, IEEE). Creating new algorithms to analyse curtailment of CER according to relevant
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application. The successful candidate will work at the intersection of multi-disciplinary modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy
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). Modelling of electrical distribution networks with VPPs and development of grid ancillary services. Implementation and proof of concept of the control algorithms in a Power HIL environment (Smart Energy
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-guided medical applications, with a focus on advanced robotics. You will work directly with clinical data to design robust, efficient deep learning algorithms that maximize the information extracted from
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algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image/video segmentation, object tracking, reinforcement learning. Deep
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 13 days ago
computationally intensive, relying on iterative time-marching algorithms to reconstruct heating environments. This limits their use in more advanced computational analyses, such as uncertainty quantification