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Field
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in solid mechanics framework Experience in non-linear solid material response and fracture modeling Experience in machine-learning modeling for solid mechanics applications Experience in
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next generation of scientists and build a workforce equipped with expertise in integrating advances in biomedical engineering, technology, and Artificial Intelligence (AI) and Machine Learning (ML
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states, charge density waves, superconductivity, and quantum magnetism - Kagome materials and superconducting hydrides - Machine learning interatomic potentials (MLIPs) and data-driven atomistic
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, dynamical systems, statistical machine learning, and neural time-series data. The goal is to better understand principles and mechanisms underlying distributed brain network computations through the dual
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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conferences. support the teaching activities at the faculty (up to 10% of the time). What we ask of you A PhD in Machine Learning, Computer Science, Mathematics, Statistics, Physics or a closely related field
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, 2026. The one-year term position is renewable for an additional year based on performance and is part of Cornell’s Active Learning Initiative . This initiative supports departments in integrating active
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an excellent work ethic and background in molecular simulation and machine learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in
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techniques (FTIR, NIR, Raman, …) with machine learning/ chemometrics. Responsibilities of the Position The Postdoctoral researcher is intended to support the soil spectroscopy research activities and digital
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness