Candidates will be responsible for working on multiple research projects under the supervision of Profs. Daniel Neill and Emily Black at NYU Courant Institute, Department of Computer Science, in the general area of AI and machine learning for social good. 75% of the candidate's effort will be supported by the NIH-funded project, "Precision Prevention Strategies for Mental Health" (PRISM), and will focus on developing novel machine learning approaches for (i) identifying heterogeneity in risk, (ii) causal inference, (iii) assessment and reduction of disparities across multiple data dimensions, and (iv) optimization of targeted health interventions, and applying these methods to mental health data in collaboration with other PRISM investigators. The remaining 25% of effort will support other research projects jointly directed by Profs. Neill and Black. While this portion of effort is flexible, many of our joint projects involve theoretical and empirical investigation of the properties of large language models, including stability, fairness, and model multiplicity.
Specific tasks may include developing and extending novel machine learning methodology, synthesizing literature, collecting experimental data, writing code, analyzing data, working on publications, and assisting with grant proposals. The candidate will be part of both Dr. Neill's Machine Learning for Good Lab and Dr. Black's Impact-Driven Evaluation for AI (IDEA) Lab and will actively participate in both labs' activities, including attending lab meetings and mentoring more junior lab members.
In compliance with NYC’s Pay Transparency Act, the annual base salary range for this position is $62,500 - $125,000. New York University considers factors such as (but not limited to) the specific grant funding and the terms of the research grant when extending an offer.
Applications will be accepted until the position is filled. We encourage applicants to submit all of the requested materials by November 1, 2026 for full consideration.
Candidates should have a PhD in Computer Science, Machine Learning, or a related field. We welcome applicants with recent PhDs and individuals seeking additional postdoctoral training. Candidates will be required to present eligibility to work in the United States. Preference will be given to candidates with demonstrated ability to develop novel machine learning methodology supported by rigorous theory, as evidenced by publications in top venues such as NeurIPS, ICML, and JMLR. We are particularly looking for individuals with expertise in the following subareas of machine learning: causal inference, algorithmic fairness, and theoretical properties of large language models (e.g., stability, fairness, and model multiplicity). Experience in working with complex data (such as health and urban data) is a plus, as is a strong interest in applying methods to impactful public health problems.
Applicants should submit, as one pdf, the following information:
All application materials should be submitted electronically via Interfolio.
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