The STELLAR project, funded by the Wellcome Trust and OpenAI, is seeking a Research Scientist to develop and validate Conceptor-based steering methods for large language models (LLMs), applied to realistic AI-powered patient simulations for mental health clinician training. Conceptors are a mathematical framework for precisely manipulating internal LLM representations, enabling continuous control over psychiatric symptom presentations that simple prompting cannot.

The position is at the NYU Stern School of Business in the Department of Technology, Operations, and Statistics. You will report to Joao Sedoc and will collaborate with other stakeholders as well as with an interdisciplinary team spanning ML, psychiatry, clinical speech, and public health.

We are looking for someone who wants to do careful, impactful work at the intersection of representation learning and clinical AI.

Responsibilities

• Develop Conceptor-based steering methods for open-weight LLMs (e.g., GPT-OSS, Llama, Gemma) to simulate psychiatric symptom presentations with precise, composable control
• Design and run evaluation experiments measuring clinical fidelity, demographic fairness, and composability of digital patient twins
• Collaborate with Penn psychiatry and LDC teams on integrating acoustic-linguistic features from clinical interview recordings
• Contribute to open-source releases of Conceptor libraries, evaluation frameworks, and model checkpoints
• Publish research at top NLP, Information Systems, ML, and interdisciplinary journals and venues (TACL, ISR, MISQ, JMLR, ACL, EMNLP, NeurIPS, IMCL, ICLR, AAAI, JAMA, npj Digital Medicine)
• Mentor graduate students and contribute to a collaborative, cross-site research environment

In compliance with NYC’s Pay Transparency Act, the annual base salary range for this position is $61,500-$102,500. 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.

Required

• PhD in NLP, information systems, machine learning, computer science, or a closely related field
• Hands-on experience with LLM internals: activations, representations, fine-tuning, and/or mechanistic interpretability
• Strong Python and PyTorch skills; comfortable working with large model weights
• Track record of publishing peer-reviewed research

Preferred

• Familiarity with steering vectors, activation engineering, or representation editing
• Experience with clinical NLP, mental health language data, or speech biomarker research
• Experience working with open-weight LLMs at scale (LoRA fine-tuning, inference optimization)
• Interest in fairness, bias, and demographic invariance in language model outputs

Apply via Interfolio and include the following:

• a cover letter
• a research statement describing research interests and accomplishments including research they would like to conduct (or have conducted) in AI and data-driven research
• a C.V.
• two letters of recommendation

Applications will be reviewed on a rolling basis until the position is filled. We aim to respond to all applicants within four weeks of submission.

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