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simulations using DFT (particularly of surface processes); kinetic Monte Carlo simulations; molecular dynamics simulations; classical and machine-learned force fields. Highly developed skills in scientific
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journals (including as the main author), such as ICSE, FSE, ASE, ISSTA, SANER, ICPC, ICSME, MSR, TOSEM, JSS, I&ST, TSE, and EMSE • Experience publishing in related fields, such as data mining and machine
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or more of the following empirical research methods will be considered an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the
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element method, wave propagation analysis, inverse problem, machine learning (ML), and artificial neural network. Strong background in research publications in the desired field. Experience mentoring other
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an advantage: applied microeconometrics and causal inference; machine learning and data science. Experience with one or more of the following computing skills will be considered an advantage: Natural
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You Completion (level A and B) or near completion (level A) of a PhD in the field of Information Retrieval, Natural Language Processing, or Machine Learning on Textual Data. Demonstrated expert
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that change! Qualifications The position requires a PhD degree in electrical, computer or biomedical engineering, computer science, or a closely related area. The successful candidate is expected to develop
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Details Title Postdoctoral Fellow in Machine Learning and Design of Biological Systems School Faculty of Arts and Sciences Department/Area Science Division Position Description A postdoctoral