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Field
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Machine Learning, Human-Computing Interactions, Social Sciences, and Public Health. Applicants should hold, or be close to completion of, PhD/DPhil with research experience in computer science, statistics
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. Ready to be part of our team? Let’s shape the future together! About the team: The Computational Materials Discovery group is looking for a postdoctoral researcher working in the field of machine learning
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analysis, data science, discrete and machine learning algorithms, distributed, intelligent, and interactive systems, networks, security, and software and database systems. The department has extensive
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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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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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from varied sources, and machine learning methodologies Required Application Materials: 1. A cover letter describing: a. Your interest in this position b. Your relevant training and experience c. Your
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information systems engineering. The group conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities and Machine Learning/AI on organisations from both
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). The emergence of data-driven techniques (broadly grouped under the term “machine learning”) challenges the traditional foundations of controls and represents an alternative paradigm that cannot be ignored
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opportunity to teach. Applicants should hold, or be close to completing, a PhD in plasma physics or high-power laser-plasma interactions. They should have extensive experience of working with particle-in-cell
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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying