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Field
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machine learning are encouraged to apply. The postdoc will also play a leading role in co-mentoring graduate and undergraduate students, assist in organizing seminars and activities related to RTG
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researchers working on hyperspectral imaging, radiative transfer modelling, machine learning, agronomy, and plant genetics. You will also work with HYDRA-EO partners in Netherlands, Spain and Italy
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research in several areas. Learning activities will focus on: The development and characterization of animal models and/or microphysiological systems for viral agents. Emphasis is placed on determining
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finite element methods, leveraging existing and actively developed libraries in AFRL related to crack growth. 4. Employ the hybrid FEM-ML framework to demonstrate a machine-learned crack model based
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, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted
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) Corrosion behavior (electrochemistry & high-temperature oxidation) In-situ monitoring of AM processes Computational skills in: Phase-field modeling, Machine Learning, FEM, DEM, COMSOL Alloy design (CALPHAD
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and machine learning, we collaborate globally to monitor environmental change and support a sustainable future. About the research project The postdoc will work at Chalmers University of Technology in a
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motivated with strong written and oral communication skills. Preferred Qualifications: Experience with first-principles calculations, DFT, Machine Learning, HPC. The individual is expected to actively pursue
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computer simulations, as well as prior work with food and other biomaterials. The application deadline is December 15, 2025. Interested applicants are encouraged to contact Juming Tang (jutang88@uw.edu
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science tools, such as machine learning methods, causal inference, particularly in economic and social science applications. Prior experience working with Large Language Models is a plus. Experience working