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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 16 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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a collaborative team of PhD-level scientists and undergraduate student, the successful candidate will conduct innovative research, publish in scientific journals and conference proceedings, mentor
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research Assistant or Associate who will work with Dr Kavanagh in his EPSRC funded lab (a team of 5 at PhD students and 1 PDRA) to understand the underlying pathophysiological mechanism of drug induced
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Commensurate with Experience Type of Position Temporary Position Time Status Full-Time Required Education PhD in neuroscience, pharmacology, or equivalent field. Required Related Experience Competitive
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, including their social, cultural, environmental, and institutional contexts. Candidates should have a PhD or comparable research experience in practice, as well as strong analytical and synthesis skills
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build on two trajectories currently underway. The first is led by a PhD researcher who investigates educational practices that protect learning in the presence of increasingly powerful automation tools
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join a highly collaborative, interdisciplinary research environment focused on developing and applying cutting-edge machine learning and deep learning methods to abdominal imaging. Working alongside
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artificial intelligence for science, knowledge-guided machine learning, and scalable scientific AI workflows. The successful candidate will conduct research at the intersection of machine learning, high
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to join an experienced mentor, lead independent projects, teach junior team members, and contribute to multidisciplinary research. We provide a supportive environment aimed at developing the scholar's
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provable guarantees AI-based cybersecurity: applying learning and AI-assisted techniques to network security, e.g., automata learning from security logs, validation of protocol models, and verified defensive