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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 23 days ago
, prompt engineering, or retrieval-augmented generation (RAG) Experience deploying large-scale machine learning models in cloud environments. Create a Job Match for Similar Jobs About The University of North
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Description Job Responsibilities: 50% - Utilize graphical/network models and data integrations, including work on collaborative projects. 10% - Assist in maintaining and upgrading, as necessary, our server
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the PhD Students: - Decide the experimental design for the validation of the developed models to authenticate pistachios, and collaborate in recruiting new samples - Perform the analytical
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healthcare and foundational biological science. The primary focus will be on designing and implementing multimodal, agentic AI models for clinical support and creating novel deep learning models for genomics
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/ML model development to design and discover redox-active materials with tunable properties (structure, charge state, etc.) Discovery of novel materials for energy storage and conversion and their
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unique research infrastructure and lab facilities to conduct world-leading fundamental and applied research within verification and model checking, embedded and cyber-physical systems, data-intensive
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training in pure dry lab experience are not a great fit to our lab. Strong background in molecular biology, gene editing (e.g., CRISPR), and genetically modified animal models is highly desirable. Additional
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properties, laboratory experiments, thermodynamic modelling of electrolytes and pilot plant operation. Responsibilities and qualifications As postdoc, you will support tasks in several different projects. You
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highly collaborative team of researchers, who are tightly integrated within IGI's Berkeley Initiative for Optimized Microbiome Editing (BIOME). Projects will focus on characterizing model microbiomes using
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the commercialisation of all-solid-state batteries. Of particular interest is the development of electro-chemo-mechanical phase field models to predict void evolution and dendrite growth (see, e.g., doi.org/10.1016