The division of Mental Health data science is a biostatistics group that includes 13 full-time biostatisticians (6 senior biostatistician faculty also affiliated with the department of biostatistics at mailman school of Public Health, Columbia University and 7 biostatistician data analysts). We provide statistical collaborative support and develop new innovative statistical methodologies relevant for the Department of Psychiatry, RFMH, and NYSPI research. We participate in extensive statistical collaborations with over 60 psychiatry researchers in any given year, providing statistical expertise and analysis to projects, including biomarkers from brain imaging and neurocognitive tasks for mood and anxiety disorders and psychosis; clinical trials of new pharmacotherapies and psychotherapies for psychiatric and neurological disorders, including building treatment decision rules; implementation studies of support service programs for mental health treatment and prevention; measurement studies for improving psychometrics of diagnosis instruments in substance use disorders, depression, and biological aging; cohort studies of child-adolescent development of psychiatric and substance use disorders; causal analysis of prescribing practices monitored from medical claims records; momentary assessment studies of cardiovascular response to emotions and stress markers related to suicide; and much more.
Division of Mental Health. www.columbiapsychiatry.org/mental-health-data-science
Research Foundation for Mental Hygiene. http://corporate.rfmh.org/
New York State Psychiatry Institute https://nyspi.org/
Position Title: Postdoctoral Fellow
Duties and Responsibilities
RFMH has an open Research Scientist position (up to 2-3 years) which provides an opportunity to develop novel statistical and machine learning methods for the next generation of data driven research in psychiatry. The postdoc associate will have opportunity to collaborate on multiple NIMH funded data science grants and innovating with cutting edge machine learning methods to solve some frontier research problem that led to innovation in psychiatric disease treatment. The available datasets to work on including processed multi modal data from ABCD study and clinical trial data for drug cessation treatment.
The candidate will have the opportunity to provide innovative solutions to problems including but not limited to: 1) Develop and apply machine learning methods for psychiatric research including and not limited to clustering, deep representative learning methods, Bayesian hieratical modeling 2) Build large generative modeling with multiple clinical trial data for drug cessation and develop the clinical utility 3) Identify special subgroup/phenotype to personalize ADHD/OCD treatment 4) Publishing papers in method/science venures.
It will prepare the candidate for future faculty positions by providing opportunities in methodology development, psychiatric application, especially in brain imaging analysis, interdisciplinary collaborative research, and grant writing, he/she can submit a K99 grant with help from faculty at NYSPI/Columbia University. We can sponsor H1B visas for excellent foreign candidates.
The postdoc fellow will have the opportunity to work closely with faculty members at the division of mental health data science at NYSPI, Ying Liu. Ph.D. (Assistant Professor of Clinical Biostatistics, Columbia University), Seonjoo Lee, Ph.D. (Associate Professor of Clinical Biostatistics, Columbia University), Yuanjia Wang, Ph.D. (Professor of Biostatistics, Columbia University), Sean Luo, MD, Ph.D. (Assistant Professor of Psychiatry, Columbia University and Melanie Wall (Director of the Mental Health Data Science and Professor of Clinical Biostatistics, Columbia University). It also provides opportunity to collaborate with leading clinical experts in NYSPI, and other data science faculties across Columbia University.
Position Qualifications:
The candidate should have a Ph.D. degree in Biostatistics, Statistics, Computer Science, Biomedical Engineering, Electrical Engineering or other related quantitative fields. Required Qualification: (a) background and training in statistical methodology development (b) proficiency in programming language such as R and Python (c) excellent oral and written communication skills. (d) Some experience with one of the following: 1) deep generative models (variational auto-encoders, transformers etc.) 2) Bayesian model.
Salary Range 75000-80000
Benefit: Common Benefit of Full time RFMH employee including paid time off and health insurance, etc.
Application Deadline: until filled.
Application Instructions.
Please send the application with a cover letter, CV, and contact information for three references to ying.liu@nyspi.columbia.edu[LS(2]