Postdoctoral Scholar in Computational Psychiatry and Digital Mental Health

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Dartmouth College: Geisel School of Medicine: Department of Biomedical Data Science

Location
Lebanon, New Hampshire

Open Date

Oct 14, 2025


Description

The research team of Dr. Nicholas Jacobson, the AI and Mental Health: Innovation in Technology Guided Healthcare (AIM HIGH) Laboratory, is seeking a motivated postdoctoral scholar to join our dynamic and interdisciplinary team. Our lab leverages digital technology, computational modeling, and AI to improve the understanding, prediction, and prevention of mental health problems, with a particular emphasis on anxiety, depression, and suicide in adolescents and young adults.

The postdoctoral scholar will play a key role in a five-year, NIMH-funded R01 study titled “Suicide Transmission Dynamics on Social Media: Modeling Risk, Understanding Platform Differences, and Exploring Ethical Implications Among Young Adults”. This project uses ecological momentary assessment (EMA) and social media data from 800 young adults to build and test deep learning models that predict rapid changes in suicidal thoughts and behaviors. A central responsibility of this position will be serving on the 24/7 on-call crisis response team, conducting clinical interviews to assess suicide risk, and managing participant safety according to the study’s comprehensive safety plan.

The lab is housed within the Center for Technology and Behavioral Health (CTBH), a P30 “Center of Excellence” funded by the National Institute on Drug Abuse. The successful candidate will have opportunities for mentorship in digital health, advanced data analytic methods (e.g., deep learning, NLP, dynamic social network analysis), manuscript preparation, and grant writing. Opportunities for clinical hours and supervision toward licensure are also available.


Qualifications

Required qualifications:

  • Candidates must have an MD (e.g., be a psychiatrist) or a PhD in Clinical or Counseling Psychology by the start of the appointment.
  • Demonstrated experience in suicide risk assessment and intervention.
  • Candidates must be proficient in quantitative data analysis, as evidenced by a track record of publications and/or presentations.
  • Candidates must have strong written and verbal communication skills.

Preferred qualifications:

  • Experience analyzing ecological momentary assessment (EMA) and/or social media data using R and/or Python.
  • Clinical or research experience in the fields of suicide, adolescent mental health, and/or computational psychiatry.
  • Interest or experience in machine learning, natural language processing, or deep learning.
  • In-person work at Dartmouth is preferred, but hybrid or remote-only applicants will be considered.

It is not expected that candidates meet all preferred qualifications. For more information regarding the AIM HIGH lab, please visit https://geiselmed.dartmouth.edu/jacobsonlab/

Dartmouth is committed to academic excellence and encourages the open exchange of ideas within a culture of mutual respect. Dartmouth welcomes people with different backgrounds, life experiences, and perspectives and believes that diversity in all its forms enhances academic excellence. Applicants should address in their cover letter how their research, teaching, service, and/or life experiences prepare them to serve Dartmouth’s commitment to academic excellence in an environment that is welcoming to all.


Application Instructions

Please apply online using this link: https://apply.interfolio.com/175523

Dartmouth College is an equal opportunity/affirmative action employer with a strong commitment to diversity and inclusion. We prohibit discrimination on the basis of race, color, sex, age, national origin, sexual orientation, gender identity or expression, disability, veteran status, marital status, or any other legally protected status. Applications by members of underrepresented groups are encouraged.

 

To help us track our recruitment effort, please indicate in your cover/motivation letter where (usajobvacancies.com) you saw this job posting.

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