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the neurovascular space. Knowledge of neurovascular anatomy, acute stroke, endovascular treatments, neuroendovascular devices for the treatment of stroke. Ability to generate machine learning analysis of medical
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diffraction data where the information extends towards 3-d space. Machine learning offers promising approaches for the solution of complex problems of disorder, ultimately aiming at general and automated
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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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addition to teaching duties, the PhD candidate is expected to conduct research in the field of (deep) machine learning, with applications in either biomedical image understanding (e.g., surgical video analysis in
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to significantly extend our existing team’s capabilities for data scoring and analysis (e.g., with expertise in natural language processing, machine learning, or computational modeling). Finally, the
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welcoming headquarters for students in the Econ PhD Program. Ability to learn new computer systems. Appointment Information This is a 100% full-time Civil Service 5002 - Program Coordinator position
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vision. Understanding or willingness to learn advanced statistical modeling is a plus Assessment criteria and other qualifications: This is a career development position primarily focused on research
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the leadership of Principal Investigator Dr Andrew Siemion. Listen's interdisciplinary research has synergies with many of the department's research priorities, including exoplanet studies, machine learning
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Sorbonne Université SIS (Sciences, Ingénierie, Santé) | Paris 15, le de France | France | 24 days ago
will focus on the following main tasks: 1) Machine-learn optimal reaction coordinates for the barnase-barstar complex, starting from ~0.1 millisecond high-dimensional MD (generated by a previous PhD
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have a PhD in Civil Engineering, Engineering Mechanics, or Mechanical Engineering. Applicants are expected to demonstrate research experience in the fields of structural modeling and machine-learning