The Max Planck Research Group for Rationality Enhancement, headed by Dr. Falk Lieder at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, is looking for a Ph.D. student (m/f/d) for a project at the intersection of machine learning and computational cognitive science.
The ability to make good decisions is an essential life skill. But making good decisions is also really hard and good decision strategies are hard to find. In this project we leverage state-of-the-art methods from machine learning and artificial intelligence to develop intelligent systems that can discover and teach the optimal strategies for different types of human decisions, such as deciding what to work on, planning a project, and managing one’s time. You will model the problem of deciding how to decide in terms of rational meta-reasoning, combine deep reinforcement learning and Bayesian program induction methods to discover optimal decision strategies, build intelligent tutors that teach those strategies, and conduct behavioral experiments to evaluate their efficacy at improving people’s decision-making skills. You will be supported by an interdisciplinary team of researchers and research assistants. The project builds on our previous work on strategy discovery, meta-level reinforcement learning, and intelligent tutors, and can rely on our infrastructure for conducting behavioral experiments.
The PhD student (m/f/d) will receive a PhD funding contract equivalent in remuneration to pay group E13, 65% of the Collective Wage Agreement for the Public Service. An initial contract will be given for 3 years with possibility of 1-year extension.
The successful applicant (m/f/d) should hold a M.Sc. degree in computer science or a related discipline (e.g., AI, machine learning, computational linguistics, statistics, or cognitive science) and have strong programming skills, and a solid background in machine learning, computational linguistics, artificial intelligence, or computational cognitive science. Experience with some of the following is a plus but not required: reinforcement learning, deep learning, deep reinforcement learning, Bayesian inference, program induction, human decision-making, web development, behavioral experiments. We would like to start this project as soon as possible but the starting date is negotiable.
The Max Planck Institute for Intelligent Systems
The Max Planck Research Group for Rationality Enhancement is part of the MPI for Intelligent Systems in Tübingen, Germany. The institute is a world-class center for foundational research in machine learning and related areas. This position is also part of the Cyber Valley. The majority of the institute’s scientific employees come from outside of Germany. You will work among gifted students and experienced scientists from all over the world; and have access to excellent infrastructure, including several regular series of tutorials, lectures, journal clubs and invited talks by international guests, as well as a large computer cluster, and dedicated full-time specialists. The working language at the institute is English.
Tübingen is a scenic medieval university town, cradled in what is simultaneously one of Germany’s most beautiful landscapes one of Europe’s most economically successful areas. Stuttgart airport is an hour by bus, Frankfurt airport can be reached in two hours by train. Most locals speak English and knowledge of German is not required to live here.
How to apply
Please email a cover letter, your CV, transcripts, your theses and/or publications to firstname.lastname@example.org and arrange for at least two letters of reference to be emailed to the same address. If you have any questions about the project, our research group, or anything else, please do not hesitate to contact Dr. Lieder at email@example.com.
The Max Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals. The Max Planck Society strives for gender equality and diversity. It seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply.