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strengths in experimental soft condensed matter physics or biophysics research within the department. Candidates with expertise in computational physics, including machine learning, applied to study soft
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 22 days ago
PhD or advanced Master’s program in economics, finance, business analytics or related fields. The chosen candidates will also gain valuable experience analyzing large data sets and learning skills in
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biology/bioinformatics, statistics, machine learning or related field. You will have a strong track record of applying genetics-based, physicochemistry-based and structure-based computational or statistical
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, silicon-proven AI/ML accelerator for transmitter error correction (digital predistortion/calibration). Your work will sit at the intersection of machine learning, DSP, and digital IC design, and you will
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, machine learning, etc. Building a quantum computer requires a multi-disciplinary effort involving experimental and theoretical physicists, electrical and microwave engineers, computer scientists, software
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outcomes Conduct applied research in areas like information extraction, machine learning, and artificial intelligence, exploring their applications in the context of social media and cross-platform
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the third Higgs boson decays to two tau leptons, or another highly sensitive combination. The student will gain expertise in machine learning techniques for signal-background discrimination and will
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to apply Website https://cv.newton-6g.eu/ Requirements Research FieldEngineering » Computer engineeringEducation LevelMaster Degree or equivalent Research FieldEngineering » Electrical engineeringEducation
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Candidates MD, DO, MBBS, PhD, EdD, or equivalent in a related field such as the health sciences, education or other field given context of work experience and/or other qualifications. Qualified for a faculty
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness