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Apply Now How to Apply Candidates interested in this position as a Post-Doctoral Research Fellow should submit a single PDF file including their CV and a cover letter describing their research
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that machine learning applications are developed with ethical considerations in mind. Participate in regular meetings with the research group. Required Qualifications* Ph.D. in Electrical Engineering, Computer
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medical, dental and vision coverage effective on your very first day 2:1 Match on retirement savings Responsibilities* Researching and developing novel machine learning architectures for integration across
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health records (EHR), waveforms from bedside monitors, radiology images and wearable sensors. This position offers a unique opportunity to work closely with clinicians on applications of machine learning
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Bioinformatics, as well as the Departments of Biostatistics & Biomedical Engineering, University of Michigan is seeking a postdoctoral fellow for bioinformatics problems involving quantum machine learning and
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manuscript(s) and/or papers from a dissertation thesis. Applications will be reviewed on a rolling basis and accepted until the position is filled. Job openings are posted for a minimum of seven calendar days
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degree of autonomy Supervise undergraduate and graduate students in the execution of assigned studies and duties. Develop close mentoring relationships to continue to promote the rich learning environment
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applicant for a Post Doc position. The candidate will work on multiple funded/unfunded research projects that aim to develop novel methods and devices to assess and improve neurological outcomes after stroke
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of applications will start as soon as possible and continue until September 30, 2025 or until the post is filled, whichever is earlier. Successful applicants will need to obtain a visa or entry permit granting
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), among others. These projects build on strong collaborations between leading, highly-collaborative research teams composed of ultrasound and MRI scientists, engineers, physician scientists in Radiology