Career Center

The Digital, Data, and Design (D^3) Institute at Harvard is accepting applications for multiple postdoctoral fellows to work on research activities at our research labs. D^3 launched in the summer of 2022 with 12 labs working on research at the intersection of academia and practice. For more information on D^3, please visit https://d3.harvard.edu.

The postdoctoral fellows will work under the direct supervision of faculty Principal Investigators and the Senior Associate Director, Lab Operations. They will work closely with the lab manager and research associate(s) at each lab. D^3 is looking for candidates with diverse backgrounds and/or new perspectives. There are no teaching requirements for these open positions.

The Trustworthy AI Lab, led by HBS Professors Hima Lakkaraju, Marco Iansiti, and Seth Neel and Harvard SEAS Professor Salil Vadhan, is seeking a Postdoctoral Fellow. The lab focuses on developing algorithms that allow data science practitioners to trade-off ethical considerations like privacy, interpretability, and bias with accuracy, and to mitigate the risks of overfitting. Recent works on fairness have included new definitions of statistical fairness that account for a more complex protected group structure or a more flexible notion of similarity, new algorithms for efficiently deleting user data from neural networks, the SOTA bounds for adaptive data analysis, and new techniques for differentially private optimization. Ensuring privacy and fairness in large-scale genomic analyses is a new research interest.

Privacy-Preserving ML (generative models, membership inference, genomic applications). The selected candidate will be expected to lead research in methodological and applied research probing privacy issues in the training and deployment of machine learning models, with a particular focus on generative models (e.g., GANs, VAEs, diffusion models, large language models etc.). We seek highly-motivated applicants with background in one or more of the following areas: generative models, differentially private learning, machine unlearning. We have a particular interest in the use of these methods on genomic data, and so experience working with large genomic datasets a plus (UK Biobank, dbGaP etc). Successful applicants will be strong technically as well as have an inclination towards real-world problems. We are looking for applicants with demonstrably strong research skills, ideally, with publications in top venues in machine learning and/or top-tier interdisciplinary journals — although this is not a hard requirement (e.g., ICML, NeurIPS, ICLR, KDD, AAAI, AI STATS, Nature/Science family of journals, PNAS). 

 Basic Qualifications:

Laura Kelley, Associate Director, Research Staff Services
Harvard Business School
Soldiers Field Road
Boston, MA 02163

Applications will be accepted until the position is filled. Please apply here via the Harvard system. Please do not contact lab faculty; if you have any questions, please contact d3@harvard.edu.

All applications should include the following:

Candidates may be asked to undergo an assessment as part of the interview process.

This is a term position through June 30, 2024, with the strong possibility of renewal based on funding and performance. Relocation funding not provided.

The University requires all Harvard community members to be fully vaccinated against COVID-19 and remain up to date with COVID-19 vaccine boosters, as detailed in Harvard's Vaccine & Booster Requirements. Individuals may claim exemption from the vaccine requirement for medical or religious reasons. More information regarding the University's COVID vaccination requirement, exemptions, and verification of vaccination status may be found at the University's

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