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multimorbidity patterns in atrial fibrillation patients. The key responsibility of the position is to structure atrial fibrillation patient’s health data for machine learning algorithms(feature engineering
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. Preferred Qualifications Applicants should have a recent Ph.D. degree in molecular biology, or protein engineering, synthetic biology, electrophysiology, advanced microscopy, optics, and/or machine learning
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the Pacific Ocean and the seismology of Antarctica. The teaching component will be mentored by Dr. Michael Wysession, Executive Director of the Center for Teaching and Learning, and will involve teaching one
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research in the form of papers, posters or oral presentations Understanding of the concepts and application of research ethics Desirable criteria Expertise in Python programming for Machine and Deep Learning
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Postdoctoral position (M/F): Machine learning design of alloys for concentrated solar energy storage
mission will be to develop machine learning models to predict properties of alloys of elements of groups 1 to 15, such as their melting temperature, range, and enthalpy. Based on these predictive models
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Details Posted: 07-Apr-24 Location: St. Louis, Missouri Type: Full-time Salary: Open Categories: Academic/Faculty Medical - Research Internal Number: JR81106 Job Description Position Summary
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the following is of high merit: - knowledge of methods for data processing of EEG and fMRI data - knowledge of statistical modeling of time series and discrete-time data - knowledge in AI and machine learning
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of statistical modeling of time series and discrete-time data - knowledge in AI and machine learning - good programming skills in MATLAB, Python, R, etc - Scientific publications in high-impact international
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of experts in machine learning and colour vision, including Dr Alexandros Koliousis (NU London), Professor Rhea T. Eskew (NU Boston), and Professor Andrew Stockman (UCL), as well as with technical
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applicant will be expected to teach in English. Qualifications Applicants must have a PhD degree or document equivalent qualifications in a relevant field related to STS, information studies or neighbouring