Position Title: Postdoctoral Fellow (computational psychiatry / data science / informatics)
Department: University of New Mexico Department of Internal Medicine, Center for Global Health
Salary: In line with NIH Postdoctoral Fellow stipend levels ($52,704 – $64,008)
Notice: Remote work from other US locations is possible.
Apply here: https://www.indeed.com/job/postdoctoral-fellow-computational-psychiatry-data-science-informatics-b68f3dc8634fe191
We are seeking to hire a Postdoctoral Fellow with data science training in support of our NIH NIMH project, “Deriving high-quality evidence from national healthcare databases to improve suicidality detection and treatment outcomes in PTSD and TBI” (PTSD – posttraumatic stress disorder, TBI – traumatic brain injury) The principal investigator of the project is Dr. Christophe Lambert, University of New Mexico, Center for Global Health, Department of Internal Medicine, Division of Translational Informatics. This research is conducted in collaboration with researchers and physicians from the University of New Mexico Departments of Internal Medicine and Psychiatry & Behavioral Sciences, Vanderbilt University Biomedical Informatics, as well as the Veterans Health Administration.
A successful candidate will join a dynamic team that spans the fields of clinical research informatics, computer science, statistics, psychiatry, and psychiatric pharmacology. Our research project aims to develop and implement machine learning approaches to detect patients with undiagnosed or unrecorded PTSD/TBI/self-harm diagnoses in patient electronic health data, to uncover patients who go undetected for these conditions and understand possible ramifications of non-treatment, including risk of subsequent self-harm. Recent related work can be read in: https://doi.org/10.1093/jamia/ocz173. Two massive national databases are available for studies: the Veteran’s Health Administration database, and the IBM MarketScan® administrative claims database, collectively covering >2M individuals with PTSD and >2M individuals with TBI. The PI and other team members are members of the OHDSI collaborative, whose vision is, “a world in which observational research produces a comprehensive understanding of health and disease”. An ideal candidate will have the drive, skills, cognitive capacity, and passion to make a large contribution towards making this vision a reality.
We believe the best combination of skills to augment our team is a data scientist with skills and interests in big data analysis, machine learning, natural language processing, biostatistics, and longitudinal observational studies. The successful candidate will spearhead running studies, and be a lead and supporting author on manuscripts. She/he should have a strong capability of distilling and synthesizing evidence from the literature to set the stage for designing each study, and have strong written communication skills to publish the study background, methods, results, discussion, and conclusions. The candidate will have the opportunity to pioneer new approaches to analyzing large and complex temporal observational datasets.
The successful candidate will have access to mentorship from world experts in clinical research informatics, statistics, neuropharmacology, and psychiatry. The candidate will participate in proposal development for future funding, and by the end of the fellowship, the candidate will have had the opportunity to make important contributions to the field of computational psychiatry.
Minimum qualifications: A PhD in statistics/biostatistics, computer science, biomedical informatics, computational psychiatry, or comparable degree.
Experience designing, analyzing, and publishing observational studies.
Experience analyzing electronic health records data and/or administrative claims data.
Experience developing research proposals.
Expertise in one or more of: machine learning, survival analysis, causal inference, and natural language processing.
Competent in SQL programming.
Competent in R programming.
Engagement in the OHDSI community and expertise in the OMOP common data model.
Have Veteran’s Administration WoC or other employee status
Required application documents:
Cover letter describing your interest in the position and relevant qualifications.
UNM is an EEO/AA employer