Working Title: Research Associate
Position Type: Professional Research Staff
Department: Department of Systems and Information Engineering
Posting Date: 08-03-2018
The Data Science Institute (DSI) and the School of Engineering and Applied Science (SEAS) at the University of Virginia (UVA) are seeking a post-doctoral research associate to perform research on projects related to deep learning and imaging and to develop new methods and algorithms that use deep learning with medical images to diagnose and predict medical conditions.
Candidates must have a PhD or equivalent degree in Data Science, Computer Science, Systems Engineering, or related disciplines. Candidates must also have expertise in supervised and unsupervised learning methods, preferably using deep learning methods including convolutional neural networks, recurrent neural networks, autoencoders, and adversarial learning. Candidates must be proficient in programming using python and associated machine learning libraries and packages and have experience in the application of machine learning techniques to image data.
In addition, the selected candidate should have excellent oral and written communication skills to present at professional meetings and to supervise undergraduate and graduate students on multiple projects along with the PI and faculty members in the DSI. The position is for one year with the possibility of renewal, based on satisfactory performance.
To apply, visit https://jobs.virginia.edu and search on posting number 0623735. Complete a Candidate Profile online, attach a cover letter, curriculum vitae, and contact information for three references. Applications will be accepted until the position is filled.
For additional information about the position, please contact Donald E. Brown at [email protected]
For information about the application process, please contact Rich Haverstrom at [email protected]
The University of Virginia is an equal opportunity/affirmative action employer committed to developing diversity in faculty and welcomes applications from women, minorities, veterans and persons with disabilities.
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