Degree: Ph.D or MD/PharmD/Ph.D.
Nationality: International Students
Application deadlines: Open until filled
The successful candidate will conduct AHRQ funded research within the field of clinical informatics focusing on patient safety event reporting systems, including but not limited to data collection, data analytics using machine learning approaches, web application development, and system maintenance. The Research Fellow will participate in designing innovative methods to analyze structured and unstructured patient safety event data using comprehensive clinical knowledge bases and advanced machine learning tools. These studies provide an excellent opportunity for the Research Fellow to work within a multidisciplinary research team and explore advanced areas in patient safety, healthcare quality related to health information technology that will prepare them for further faculty of industry positions.
In addition, the Research Fellow will have the opportunity to propose research projects that will be suitable for external funding. The Research Fellow will be encouraged to prepare and submit a career development award proposal, as well as other research proposals as appropriate, and to lead publications. The Research Fellow will have the opportunity to enroll in the Postdoctoral Certificate Training Program provided by the Postdoctoral Affairs Office of UTHealth at no cost. The participants receive training on supervised research, career development, communication and teaching skills, and a number of benefits including a free UTHealth Recreation Center membership and discount on several campus and city services.
- Biomedical informatics, computer science, information science, computational linguistics, data mining, or a closely related field;
- Nursing with demonstrated strength in Biomedical Informatics research.
- Highly motivated, ability to identify potential problems and develop solutions.
- Ability to plan and carry out research experiments and projects.
- Strong written and oral communication skills
- Works independently and participates productively as a team member.
- Familiarity with machine learning and statistical methods.
- Experience in the areas of medical terminologies/ontologies, natural language processing/computational linguistics, or machine learning is preferred.
- Experience in computer programming languages (e.g., Python, Java, C#, etc.) is preferred.
Application materials should include:
- Cover letter
- Three letters of reference preferred, or contact info of references
- Sample publication with clearly stated individual contributions
Please send application materials to [email protected]