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satisfactory performance and funding availability. Applicants should possess a Ph.D. degree in epidemiology, biostatistics, applied mathematics, data science or other related disciplines. They should have strong
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into the ethical governance of Large Language Models (LLMs), as part of the prestigious Divirsibus Vis Plurima Solvo project. The position is full-time and fixed term for 41 months or to the funding end date of 30
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Stroke Research and Prevention Group of HKU (stroke.hku.hk) to assist in neuroimaging data acquisition and analysis, write-up of the results, as well as training of students Apply image analysis skills
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on collating and analyzing the large volumes of carbon cycle data gathered from the site to date, then preparing the resulting analyses for publication in scientific journals. Likely topics for papers include
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. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics for real-time battery health monitoring and fault detection. Collaborate with
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for interacting with colleagues and stakeholders. Department Specifics Develop various machine learning and data mining models including convolutional neural networks (CNNs), Transformers, large language models
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. The successful candidate will join the Experimental High Energy Physics group led by Profs. Brinkerhoff, Dittmann, Hatakeyama, and Wilson to participate in the Compact Muon Solenoid (CMS) experiment at the Large
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information at: http://www.ifw-dresden.de . The Institute for Metallic Materials (Prof. K. Nielsch) of the IFW Dresden offers Postdoc position (m/f/d) on the following topic: Thermoelectric Energy Harvesters
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& Sciences Campus Main - Albuquerque, NM Benefits Eligible Postdoctoral Fellows may be eligible to receive certain UNM benefits . See the Benefits home page for more information. Position Summary We invite
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(Python, MATLAB) for processing and analyzing large or multimodal datasets (e.g., neural activity and behavior). Experience working with quantitative behavioral or electrophysiological data collection