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-authorship on scientific papers, presenting results, and representing research at meetings. Knowledge of machine learning, data mining, and analytic techniques. Conditions of Employment: Weekend and evening
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intelligence models for the analysis of multispectral remote sensing imagery. The main tasks include implementing computer vision and machine learning methods for the detection and prediction of algal blooms in
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of Environmental Engineering, ETH Zürich and matriculate in ETH Zürich. The research is related to development of experimental and modeling techniques to identify emission sources, simulate the airborne transport
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environment where machine learning meets real-world scientific impact. What You’ll Do: Conduct cutting-edge research at the intersection of AI and science Develop large-scale deep learning models for scientific
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demonstrated track record in protein structure modelling methods, with hands‑on experience in protein or biologics design and engineering. Hands‑on experience with common machine learning / deep learning
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in modelling, simulation, or data analysis of energy systems Knowledge of machine learning or artificial intelligence methods Programming experience (e.g., Python, MATLAB or similar tools) Experience
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. The project focuses on developing an integrated approach that combines machine learning techniques with physics-based models to estimate the health of various system components. The aim is that fault diagnosis
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for a full-time, on-site PhD position in machine learning, forecasting and time series analysis. Reykjavik University, Department of Engineering. Duration: 3 years. Start date: Negotiable. Reykjavik
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the integration of modern machine learning tools into computational catalysis workflows. Write research proposals for funding and manuscripts for publication. Assist the PI with the supervision of students and
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 9 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward