Job:Post-Doctoral Research Fellowship Opportunity National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH)
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5.2 years ago

Post-Doctoral Research Fellowship Opportunity National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH)

Post-Doctoral Fellow: The National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), Department of Health and Human Services (DHHS), is recruiting a postdoctoral fellow to study individual and environmental factors in drug taking and relapse. The primary focus is on collecting real-time self-reports of exposure to drug cues and psychosocial stressors via Ecological Momentary Assessment and Social Media Language; these data are to be integrated with continuously collected GPS and smartphone sensors data to gain a better understanding of relapse and long-term recovery. The workplace is in the Clinical Pharmacology and Therapeutics Research Branch of NIDA's Intramural Research Program (IRP) located in Baltimore, Maryland.

Candidates should have graduate-level expertise in natural language processing and machine learning. Experience with GIS (Geographic Information Systems) and Change Point Detection is highly desirable, but not required. The successful candidate must have an M.D. or Ph.D. in Computer Science, Information Science, Epidemiology Psychology, or a closely related field, with 5 or fewer years of postdoctoral experience. Stipend will be commensurate with experience and previous accomplishments. The position is open to both U.S. and non-U.S. citizens.

To apply, please send a CV, statement of research interests, 3 representative publications or working papers, and three letters of recommendation to:

Brenda L. Curtis, Ph.D., NIH/NIDA/IRP
251 Bayview Blvd., Suite 200,
Baltimore, MD 21224, USA

Email: brenda.curtis@nih.gov.

Applications will be accepted until the position is filled.

DHHS,NIH and NIDA are Equal Opportunity Employers. Applications from women, minorities, and persons with disabilities are strongly encouraged.

NLP Machine-Learning GIS • 1.5k views
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