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for nanopore-based VOC analysis in collaboration with environmental scientists. The work aims to apply cutting-edge molecular sensing approaches to ecosystem monitoring and environmental diagnostics. We
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, integrate device engineering with clinical workflows, and apply artificial intelligence and machine learning for automated image and signal analysis, tissue classification, and real-time diagnostics
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computational datasets of disordered materials based on density functional theory calculations and training machine learning models to accelerate the predictions. This work will involve collaboration with Assoc
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Description The Robot Learning & Control Lab (REAL Lab) at NYU Abu Dhabi is seeking an outstanding Post-Doctoral Associate to contribute to cutting-edge research in robot intelligence, machine
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computational imaging specialist – experience in quantitative image analysis, scattering modeling, signal processing, machine learning, or neural-network-based data interpretation. The project is closely
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
to develop applied research skills in machine learning, interact with an international network of collaborators, and gain post-doctoral research experience. The ideal candidate is self-motivated and can work
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Associate will collaborate with instructors of Environment and Sustainability (E&S) core courses to: develop and integrate active-learning strategies that strengthen systems-thinking competencies; design
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communication skills, including the ability to translate statistical results for clinical audiences and to teach non-statisticians. Brief Description of Duties: The Postdoctoral Associate will work under the
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that you are eligible for, including health insurance, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link
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. We expect successful applicants to work towards improving the collaborations and connections among the different areas. We invite applicants to visit our website to learn more about current research