Why build AI systems that replace people if we can build AI systems that collaborate with people? Hybrid Intelligence is the combination of human and machine intelligence, expanding human intellect instead of replacing it. Our goal is to design Hybrid Intelligent systems, an approach to Artificial Intelligence that puts humans at the centre, changing the course of the ongoing AI revolution. The project will be recruiting 27 PhD or postdoc positions in total. For more information on the project see The Hybrid Intelligence Centre.

At the University of Amsterdam, we are looking for a PhD candidate interested in combining (deep) reinforcement learning research with prior knowledge from knowledge graphs to learn explainable strategies for complex sequential tasks. 

Many problems of practical interests are concerned with optimising sequential decision making. Think, for example, of finding optimal trajectories for vehicles or robots or deciding which medical tests to run. Methods for classical planning based on symbolic representations are typically explainable to human collaborators but rely on an exact problem description; while data-driven (e.g. reinforcement learning) approaches do not rely on a provided problem specification, but are data hungry and inscrutable. The combination of the complementary strengths of these approaches is expected to advance the state of the art in the areas of knowledge representation and reinforcement learning.

What are you going to do?

You are expected:

• to invent, evaluate and describe novel algorithms for integrating symbolic prior knowledge with (deep) reinforcement learning techniques, with an aim to develop explainable strategies for sequential behavior;
• to integrate such algorithm into an experimental scenario, and evaluate according to quantitative and qualitative indicators including efficiency, explanation quality and trustworthiness, user’s satisfaction; 
• to present research results at international conferences, workshops, and journals;
• to become an active member of the research community and to collaborate with other researchers, both within and outside the Informatics Institute;
• to pursue and complete a PhD thesis within the appointed duration of four years;
• to assist in teaching activities, such as teaching labs and tutorials or supervising bachelor and master students.

• A Master’s degree in Computer Science, Artificial Intelligence, Machine Learning or a closely related field;
• you have a strong scientific and mathematical background in artificial intelligence. Familiarity and interests with knowledge representation or reinforcement learning is preferable;
• a good academic record and eagerness to tackle complex scientific problems;
• you are able to implement and evaluate learning algorithms, e.g. using Python deep learning toolkits;
• you are able to work well in teams and communicate fluently in written and spoken English.

Our offer

A temporary contract for 38 hours per week for the duration of 4 years (initial appointment will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years) and should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and master students. 

The salary, depending on relevant experience before the beginning of the employment contract, will be €2,325 to €2,972 (scale P) gross per month, based on full-time employment (38 hours a week). These amounts are exclusive 8 % holiday allowance and 8,3 % end-of-year bonus. A favourable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants.  The Collective Labour Agreement for Dutch Universities applies. 

Are you curious about our extensive package of secondary employment benefits like our excellent opportunities for study and development? Take a look here.

University of Amsterdam

With over 5,000 employees, 30,000 students and a budget of more than 600 million euros, the University of Amsterdam (UvA) is an intellectual hub within the Netherlands. Teaching and research at the UvA are conducted within seven faculties: Humanities, Social and Behavioural Sciences, Economics and Business, Law, Science, Medicine and Dentistry. Housed on four city campuses in or near the heart of Amsterdam, where disciplines come together and interact, the faculties have close links with thousands of researchers and hundreds of institutions at home and abroad.  

The UvA’s students and employees are independent thinkers, competent rebels who dare to question dogmas and aren’t satisfied with easy answers and standard solutions. To work at the UvA is to work in an independent, creative, innovative and international climate characterised by an open atmosphere and a genuine engagement with the city of Amsterdam and society.

Faculty of Science - Informatics Institute

The Faculty of Science has a student body of around 7,000, as well as 1,600 members of staff working in education, research or support services. Researchers and students at the Faculty of Science are fascinated by every aspect of how the world works, be it elementary particles, the birth of the universe or the functioning of the brain.

The mission of the Informatics Institute is to perform curiosity-driven and use-inspired fundamental research in Computer Science. The main research themes are Artificial Intelligence, Computational Science and Systems and Network Engineering. Our research involves complex information systems at large, with a focus on collaborative, data driven, computational and intelligent systems, all with a strong interactive component.

The PhD candidate will be employed full time at UvA, within the AMLAB (Amsterdam Machine Learning Lab) research group that is headed by Prof. Max Welling. Daily supervision will be provided by Herke van Hoof, with additional supervision provided by Ilaria Tiddi and Prof. Frank van Harmelen at the Vrije Universiteit Amsterdam. The AMLAB consists of about 30 people working on various topics within machine learning, including deep learning, reinforcement learning, causality, topological methods, Bayesian methods, etcetera. Within the lab, collaboration is stimulated, as is interaction between group members in formal settings (e.g. seminars) as well as informal setting.