Postdoctoral Associate - Natural Language Processing and Machine Learning (56226)



We are recruiting a Postdoctoral Associate with a focus on Natural Language Processing (NLP) and Machine Learning (ML). The successful applicant will have a Ph.D. in biomedical informatics (BMI), computer science or a related field. Working experience with clinical natural language processing, machine learning (deep learning) are preferred. This position will work under the guidance of and in collaboration with Dr. Yonghui Wu to fulfill the research and service missions of the BMI program. In particular, the incumbent will assist with NLP methods development, clinical data processing, predictive modeling using machine learning methods, NLP package development, scientific paper writing and presenting at national/international conferences. In addition, the incumbent will participate in analysis and reporting projects through data summarization and visualization, and contributions to analysis, modeling and interpretation of results.

  • Develop novel clinical natural language processing methods to extract information from clinical narratives; apply machine learning methods (especially deep learning methods) to solve clinical NLP problems.
  • Implement the NLP methods into a software that meets research needs, manage and update source codes as needed. Work in an interdisciplinary team of informaticists, programers, information quality experts, statisticians, and researchers during software development.
  • Participate in design, implementation, and reporting of research and evaluation studies.Contributions to scientific reports, conference papers and journal articles are expected, including documentation of method, presentation of data, and participation in interpretation of results.
  • Participate in collaborative projects, contribute to the data processing, software development, and other collaborative efforts.


Minimum Qualifications:

  • Ph.D. in biomedical informatics, computer science or a related field. Working experience with clinical natural language processing, machine learning (deep learning) are preferred.


Preferred Qualifications:

  • Experiences in natural language processing, machine learning (deep learning), and software development are preferred. Excellent formal and interpersonal communication skills, and the ability to communicate effectively to a broad range of audiences such as faculty members, PIs.
  • Python, Java, or similar language experience.
  • Familiar with popular machine learning packages, e.g., support vector machines (SVMs), conditional random fields (CRFs).


Applicants should upload the following items:

  • Contact information for three references
  • Letter of interest
  • CV
  • Relevant writing sample


The Search Committee will accept applications until the position is filled. Applications will be reviewed starting as soon as possible after the posting date.

Selected candidate will be required to provide three letters of recommendation and an official transcript to the hiring department upon hire. A transcript will not be considered "official" if a designation of "Issued to Student" is visible. Degrees earned from an education institution outside of the United States are required to be evaluated by a professional credentialing service provider approval by National Association of Credential Evaluation Services (NACES), which can be found at http://naces.org/ .

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If an accommodation due to a disability is needed to apply for this position, please call 352-392-2477 or the Florida Relay System at 800-955-8771 (TDD). Hiring is contingent upon eligibility to work in the US. Searches are conducted in accordance with Florida's Sunshine Law.

The University of Florida is committed to non-discrimination with respect to race, creed, color, religion, age, disability, sex, sexual orientation, gender identity and expression, marital status, national origin, political opinions or affiliations, genetic information and veteran status in all aspects of employment including recruitment, hiring, promotions, transfers, discipline, terminations, wage and salary administration, benefits, and training.

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