Computer Science Postdoctoral Fellow

Position Description

The Laboratory for Innovation Science at Harvard University (LISH) is accepting applications for a computer science postdoctoral fellow starting in 2022. Candidates with a background in one or more of the following areas are encouraged to apply: machine learning, explainable ML, adversarial ML, fairness and differential privacy, statistical learning theory, causal inference. Successful applicants will be strong technically as well as have a background or interest in real-world problems.

LISH is a Harvard-wide research program led by faculty co-directors Karim Lakhani and Marco Iansiti of Harvard Business School; Eva Guinan, Harvard Medical School; and David Parkes, Harvard School of Engineering and Applied Sciences. The lab works with several partners (NASA, Harvard Medical School, and various other institutions and corporations) to investigate real-world innovation problems. LISH is uniquely positioned to partner with the Harvard School of Engineering and Applied Sciences over the next five years, working both with faculty and companies tackling digital, data, and design problems.

The postdoctoral fellow will work with an interdisciplinary lab of social science researchers to investigate questions around the development and deployment of digital technology. They will apply and develop machine learning methodologies to questions of interest in economics and business. Fellows are encouraged to collaborate with statisticians and computer scientists around the university. Postdoctoral fellows in the past have worked with enterprises on problems related to pricing algorithms and matching.

The ideal candidate will have:

  • Familiarity with methodological foundations, for example: algorithmic economics, matching markets, contest design, causal inference, data systems design, deep learning, experimental design, modeling of structured data, non-parametric Bayesian methods, scalable inference, statistical computation, and visualization.
  • Demonstrably strong research skills, ideally with publications in top venues in machine learning, artificial intelligence, or sister conferences (e.g., ICML, NeurIPS, ICLR, KDD, AAAI, IJCAI, UAI, FAccT, AIES, AIStat, ACMEC, WINE), and/or top-tier interdisciplinary journals (e.g., Nature family of journals, PNAS, Science).

Depending on projects, the postdoctoral fellow will collaborate with one or more LISH faculty co-directors: Karim Lakhani, Eva Guinan, David Parkes, and Marco Iansiti, and/or other LISH-affiliated faculty including: Iavor Bojinov, Kris Ferreira, Hima Lakkaraju, Edward McFowland, and Seth Neel.

Basic Qualifications:

  • A Ph.D. or equivalent degree in Computer Science or a closely related field (e.g., Statistics, Applied Mathematics, etc.). Please note: If you have obtained your Ph.D. in the past 12 months, you must be able to provide a certificate of completion from the degree-granting institution or a letter from your institution’s registrar stating all requirements for the degree have been successfully completed and to verify the date your degree was conferred.

Additional Qualifications:

  • Strong programming skills and experience with machine learning and its applications.
  • Strong team player with excellent communication skills.

Application Details:

Applications will be accepted until the position is filled. Please email the following lish@harvard.eduwith the subject “Computer Science Postdoctoral Fellow”. All applications should include the following:

  • Curriculum vitae
  • Copy of academic records (unofficial records are acceptable
  • 2-page research statement
  • Two research paper
  • Contact details of at least two references

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

Appointment Details:

This is a one-year term appointment through Harvard University with the possibility of renewal based on performance and funding. Relocation funding not provided.

Harvard is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, sex, gender identity, sexual orientation, religion, creed, national origin, ancestry, age, protected veteran status, disability, genetic information, military service, pregnancy, and pregnancy-related conditions, or other protected status