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Field
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Engineering Department at The Pennsylvania State University. This position involves the development and application of numerical analysis approaches using Machine Learning based multi-physics tools
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. Preference will be given to candidates with a strong background in gravitational wave detection, gravitational wave cosmology, gravitational wave data analysis, and/or numerical relativity simulations
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the development, testing, and analysis of resilient geotechnical systems. The work will rely on centrifuge modeling, geotechnical laboratory testing, and complementary numerical simulations to improve understanding
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on advanced timing and frequency control technologies. Core Responsibilities: Conduct research on MEMS quartz resonators and precision micro-oscillator technologies Develop analytical, numerical, and finite
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conferences. Mentorship & Service: Assist in mentoring graduate and undergraduate students in control theory, machine learning, data analysis, and software implementation; assist in grant proposal development
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Computational Social Science, with a starting date of September 1, 2026. Some of the possible research topics include: (i) social media analysis, (ii) collaboration and teamwork, (iii) gender inequality, (iv
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models
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univariate and multivariate statistical analysis methods and with bioinformatics tools, including MATLAB, SIMCA, R, Python, MetaboAnalyst, KEGG, Ingenuity Pathway Analysis and other software related to omics
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involving multiple cw and pulsed lasers, ion-beam optics, ion traps, rotational and electronic molecular spectroscopy, ultra-high vacuum, magnetic measurement and shielding, cryogenic design, numerical
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concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical