Positions Available

A fully funded, two-year postdoc with competitive salary is available.

The research will be in the area of advancing deep reinforcement learning towards continual learning. Responsibilities include helping to design methods to enable agents to learn a variety of new tasks without losing the ability to perform previous ones (i.e. avoiding “catastrophic forgetting”) and testing these method on both simulated and physical robots (you will have primary access to 3 Fetch Research robots to perform your experiments).

This position will be advised by Professors Jeff Clune and Nick Cheney. It is available immediately, and applicants will be considered on a rolling basis – though some flexibility on start date is possible. Experience in deep learning and/or reinforcement learning is desired. But applicants with strong research backgrounds in relevant fields (e.g. statistics, math, computer science), strong programming skills, and a desire to learn more about machine learning and neural networks will also be considered.

The Evolving Artificial Intelligence lab focuses on robotics and creating artificial intelligence in neural networks, either via deep learning or evolutionary algorithms. The lab is actively engaged in research. Just in the last three years, we have received an NSF CAREER award, participated in a cover article in Nature on robotics, produced the 63rd most talked about scientific paper worldwide in 2015, and our deep learning papers were awarded oral presentations (1-6% acceptance rates) at NIPS, CVPR, ICLR, and an ICML workshop. In that same time, we also produced six papers at GECCO, including a best paper award, and three papers in PLoS Computational Biology – including a cover article with a Reddit AMA about it that over 42,000 people joined to ask 1000+ questions. Additionally, our work has been covered by almost every major international news outlet.

All candidates must apply through the following website:
Apply for the Postdoctoral Research Associate Position in the Evolving AI Lab

Unfortunately we are not able to accept new PhD students at this time.

Application Instructions for PhD Students

If you are interested in joining the lab, please email the following information to jeffclune@uwyo.edu:

  • Your CV (including a list of publications)
  • Grade Point Averages (including a conversion to the US 4.0 scale)
  • GRE scores (the percentile scores, not the raw scores)
  • A list of references (their names, titles, institutions, and how you know them)
  • A list of courses relevant to conducting research (e.g. courses in statistics, math, computer science, biology, etc.).
  • A link to a video of yourself where you describe (a) your motivations for obtaining a Ph.D., (b) which of the above research areas particularly interest you, and (c) why you think our lab is the right fit for you. It is alright if you do not know what subject you want to study at this point, but it would help to get a better sense of your interests. The reason for it being a video is to assess your communication skills.
  • Any other material you feel communicates your research background (e.g. videos of your projects).

Thanks for your interest in the lab. It is a fun, exciting place where you will have a lot of freedom to pursue cutting-edge research in the areas of artificial intelligence that you find fascinating.

About the University of Wyoming

The University of Wyoming is located in Laramie, a college town in the heart of the Rocky Mountain West. Nestled between two mountain ranges, Laramie has more than 300 days of sunshine a year and is home to year-round outdoor activities including hiking, camping, rock climbing, downhill skiing, cross-country skiing, fishing and mountain biking. Laramie is also near many of Colorado's major cities and university communities (Fort Collins: 1 hour; Boulder: 1.5 hours; Denver: 2 hours).

You can watch a funny, but helpful video about living in Laramie on YouTube. More information about living in Laramie can be found here.

The University of Wyoming is an Affirmative Action/Equal Opportunity employer. All qualified applicants receive consideration for employment without regard to race, color, religion, gender, pregnancy, sexual orientation, age, national origin, disability, marital, veteran or any other legally protected status.