Are you interested in machine learning for intelligent decision making? Would you like to overcome fundamental challenges and bridge the gap to physical agents like robots, autonomous cars or automated factories? Create new cutting edge neural network architectures to empirically solve theoretical problems in artificial intelligence? Think about broader implications and practical applications?

Deep reinforcement learning (RL) has been at the core of many recent success stories in AI, in particular for playing strategic games like Go, Chess and StarCraft. Despite those spectacular breakthroughs, RL is rarely used in practice, as the learned control policies are generally not assumed to be reliable enough for deployed robots or autonomous cars. We want to change that!

During your PhD, you will develop new algorithms which generalize to situations that differ significantly from training. This will be possible by controlling a graph neural network's internal epistemic uncertainty, that is, how much the network trusts it's own computations. You will evaluate your work on multi-task RL benchmarks, where the agent learns one policy that is able to solve more than one task. Examples are simulated Mujoco robots or different levels of the same computer game. Your challenge will be to study, propose and empirically verify the properties that improve generalization in these benchmarks. You will be collaborating with other members of the Algorithmics group that work on related projects. There might also be an opportunity to transfer your work to real robots, if you are interested

You hold an MSc degree or a similar degree with an academic level equivalent to a two-year master's degree in either Computer Science, Artificial Intelligence, Mathematics or a similar area and have an interest in Deep Reinforcement Learning. You want to know more about fundamental questions in autonomous control and enjoy empirically validating theoretical predictions and hypotheses. You possess a strong background in machine learning, and have good programming and math skills. Familiarity with Python and a deep learning framework like PyTorch or TensorFlow (and potentially links to past projects) are appreciated. Prior research experience, in particular in thematically relevant or particularly interesting projects, is a bonus.

Fixed-term contract: 4 years.

TU Delft offers PhD-candidates a 4-year contract, with an official go/no go progress assessment after one year. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2434 per month in the first year to € 3111 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.

Delft University of Technology

Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context. At TU Delft we embrace diversity and aim to be as inclusive as possible (see our Code of Conduct). Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale.

Challenge. Change. Impact! 

Faculty Electrical Engineering, Mathematics and Computer Science

The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three disciplines - electrical engineering, mathematics and computer science. Combined, they reinforce each other and are the driving force behind the technology we use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make future-proof. We are also working on a world in which humans and computers reinforce each other. We are mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. There is plenty of room here for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1,100 employees and 4,000 students work and study in this innovative environment.

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