Open Research Positions

I have openings for the following research positions:

Multiple Postdocs in Lifelong Machine Learning, Service Robotics, and Social Network Analysis
University of Pennsylvania

Applications will be reviewed as they arrive, starting immediately, and will be accepted until the positions are filled. For full consideration, we recommend that you apply early.

The Lifelong Machine Learning group within the General Robotics, Automation, Sensing and Perception (GRASP) Laboratory at the University of Pennsylvania invites applications for multiple full-time postdoc openings on the following topics:

  • Lifelong Machine Learning (possibly including Deep Learning) - This postdoc will conduct research to develop the next generation of lifelong machine learning algorithms, capable of continual learning over consecutive tasks.  The postdoc will also collaborate with various other research groups and collaborators (e.g., Peter Stone at UT Austin, Fei Sha at USC, Satinder Singh Baveja at UMichigan, and Michael Littman at Brown University).  The ideal candidate would have a background in lifelong learning, deep learning, transfer learning, multi-task learning, non-stationary processes, and/or Bayesian methods.
  • Lifelong Machine Learning and Service Robotics - This postdoc will conduct research on lifelong learning and its application to autonomous mobile service robots that operate continually in university, commercial, and home environments.  This postdoc will also work with our other collaborators, as listed above.  The ideal candidate would have a background in robotics, lifelong learning, transfer learning, multi-task learning, and/or computer vision/perception.  The applicant should be comfortable working with robotic hardware and be experienced in ROS.
  • Transfer and Multi-Task Learning in Social Network Analysis - This postdoc will work on developing methods for transfer and multi-task learning in large-scale relational data.  The postdoc will also be expected to collaborate with Larry Carin's group at Duke University.  The ideal candidate would have a background in relational learning, social network analysis, transfer learning, and/or multi-task learning.

In addition to conducting research on the above topics, the Fellows will have opportunities for mentoring students, assisting in the grant proposal process, and broad exposure to other researchers working on these projects.  For further information on our work in lifelong machine learning and our recent progress, please see the following papers: ICML'13, IJCAI'15 (nominated for best paper), and IJCAI'16 (awarded best student paper), among others on the Publications and Research pages.  Videos of our (current) autonomous service robots are available on the Research page, though we will be working with more sophisticated robots in the future.

These positions are available immediately and will be renewable for up to 4 years. A Ph.D. in computer science, machine learning, robotics, physics, or another closely related field is required. The position will report to Dr. Eric Eaton. The compensation package includes a competitive salary, health benefits, career mentoring, and conference travel support.

Candidates should have a strong background in machine learning and/or robotics, supported by a solid publication record in top-tier machine learning, AI, and robotics conferences (e.g., NIPS, ICML, AAAI, IJCAI, IROS, ICRA). Candidates should be familiar with Java, C/C++, and/or Matlab programming in Linux-based environments, and other machine learning (e.g., Tensorflow, Keras, Torch) or robotics toolkits.

To apply, e-mail a single PDF file containing (1) a cover letter, (2) curriculum vitae, (3) research statement, (4) list of representative publications with URLs, and (5) list of references directly to Dr. Eric Eaton ( ) with a subject line of "Postdoc Application: YOUR NAME". Please have 2-3 letters of recommendation sent directly to Dr. Eaton at with a subject line of "Postdoc Reference: YOUR NAME".

The University of Pennsylvania is an Ivy League University located near the center of Philadelphia, the 5th largest city in the US. The University campus and the Philadelphia area support a rich diversity of scientific, educational, and cultural opportunities; major technology-driven industries such as pharmaceuticals, finance, and aerospace; as well as attractive urban and suburban residential neighborhoods. Princeton and New York City are within commuting distance.

The University of Pennsylvania is an Equal Opportunity/Affirmative Action Employer. Minority candidates and women are especially encouraged to apply. Hiring is contingent upon eligibility to work in the United States; if you are not a US-citizen and hired, Penn will work with the U.S. Government to obtain the necessary work permits. Questions can be directed to Dr. Eric Eaton.