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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
on Qualifications Proposed Start Date Estimated Duration of Appointment 12 Months Position Information Be a Tar Heel! A global higher education leader in innovative teaching, research and public service
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College London. • Develop skills in relevant methods to assist the research. • Contribute to project reporting and scientific deliverables. • Support the supervision of students and junior
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expertise in specialised domains where labelled data is scarce. Recent foundation models have transformed computer vision, yet their ability to acquire new expertise remains limited when training data is
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process modeling, simulation, and systems analysis Experience with experimental work in chemical or process engineering (desirable) Knowledge of process optimization, parameter estimation, or control
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curious to deliver work that matters, your journey starts here! The Department of Electrical and Computer Engineering (ECE) ranks among the best in the country. Our research programs are at the forefront
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Biology is desirable. Experience in the application and development of computational methods/tools or machine learning algorithms. Good computer programming skills in R/Matlab/PerlPython. Baylor College
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experimental work in chemical or process engineering (desirable) Knowledge of process optimization, parameter estimation, or control methods is a plus Programming experience in MATLAB and/or Python Familiarity
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, with a second year of funding available dependent on performance. For non-US citizens, ability to obtain a work permit or visa is required. This position has an estimated duration of two (2) years
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methods, provides the ideal environment for research on programming with formal guarantees — a topic of increasing practical importance. About us The Department of Computer Science and Engineering , a joint
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developing next-generation AI methods for healthy climate adaptation. The position will focus on building and evaluating foundation models for large-scale spatiotemporal health and environmental data. Our team