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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 3 hours ago
performance utilizing a variety of tools including life-cycle assessment (LCA), machine learning techniques, and/or optimization methods. This position will carry out state of the art research into the economic
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outcome sets. You will either need a PhD (or be nearing completion), in computer science, data science, artificial intelligence/machine learning (AI/ML), health data science, bioinformatics or a related
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PostdoctoralResearcher in Radio Frequency Fingerprinting using Machine Learning who has: strong experience in signal processing, machine learning or a related field a PhD (or near completion) in signal processing, machine
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using Machine Learning who has: strong experience in signal processing, machine learning or a related field a PhD (or near completion) in signal processing, machine learning or a related field experience
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astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, in particular simulation-based
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Eligibility criteria Selection will be based on the following scientific and technical criteria: • PhD in computational biology, machine learning, bioinformatics or a related field. • Proficiency with Python
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, Organometallic, Organic Chemistry and Machine Learning for a period of up to 24 months. The project, funded by EPSRC, will involve exploring the use of machine learning to develop new tools for investigating
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successful in this role, you will hold (or be close to completing) a PhD/DPhil in machine learning, artificial intelligence, computer science, epidemiology, health data science, or a related quantitative
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: Durham, NC – Nicholas School of the Environment. Required Qualifications at this Level Education/Training Work requires a PhD or equivalent doctoral degree in the social sciences, including science, public
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checks with advanced machine learning architectures, specifically Long Short-Term Memory (LSTM) networks and Variational Autoencoders (VAEs). The researcher will use historical QC archives dating back