2,352 machine-learning "https:" "https:" "https:" "https:" "UCL" "UCL" Postdoctoral positions in United States
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data issues. Utilizing machine learning techniques as appropriate for data analysis. Developing computing programs and software to support research initiatives. Applying new methodologies to real-world
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. Additional background in renewable energy, surface science, catalysis, and/or machine learning. Strong programming skills in Python and some exposure to machine learning. Ph.D. in Materials Science, Physics
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addition to this load, successful candidates are expected to conduct research in collaboration with CMU-Q faculty or local partners. We are particularly interested in candidates in the areas of Computer Systems
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, nutrition lesson) and bi-weekly they will engage in a self-guided culinary session at home (prepare an ethnic, plant-based meal). To learn more visit https://www.aceprogramnyc.com/ . 2) The Double Up Foods
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renewal. The Fellow will be expected to be in residence, to conduct research in Duke's library and archival collections, to participate actively in the intellectual life of the university, to teach up to 2
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Professor John Harlim and his collaborators, Yan Li in the Electrical Engineering department, and Daning Huang in the Aerospace Engineering department in the area of Scientific Machine Learning. The project
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spearhead technical development, data analysis, and publication efforts for a student-led initiative using OpenBCI hardware, EEG recordings, eye-tracking integration, and machine learning to enhance neural
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Posting Summary Logo Posting Number RTF00037PO26 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/156387 Business Title (Internal Title) Post Doctoral
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strongly encourage applications from candidates with diverse backgrounds, including those from groups that have been underrepresented in academia. To apply, visit https://wesleyan.wd5.myworkdayjobs.com
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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting