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management tools. This role involves designing and executing large-scale epidemiological studies using EHR data, overseeing clinical data annotation/processing from public and Hospital Authority sources, and
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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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will work on multiple projects funded by NIH/NHGRI. The objective of the position is to develop novel statistical methods and computer software and analyze large scale biological data from biobanks
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develop innovative solutions based on the analysis of urban data (big data, IoT, GIS) to monitor and improve public health. You will contribute to modeling smart cities with a focus on health and designing
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experience with AI model development, including fine-tuning, applying and evaluating large language models for tasks such as information extraction, summarization, and other biomedical or clinical applications
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, developing research proposals, and helping with graduate student mentoring and group organization. Opportunties to Contribute Analyze and interpret data to support collaborative discussions and decision-making
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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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available within the Department of Medicine, Division of Endocrinology in the Laboratory of Brain Behavior and Big Data at the University of Southern California. The applicant with work with PI and
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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