The Informatics Institute has a full-time vacancy for an Assistant professor Deep Reinforcement Learning.

Machine learning, and in particular deep learning has become a driving force behind many recent breakthroughs in artificial intelligence. From speech recognition, image analysis, natural language understanding, machine translation, query and answering systems, protein folding, and even playing GO, deep learning is breaking records. These exciting developments have not gone unnoticed to industry either: Google, Facebook, IBM, Qualcomm, Yahoo, Microsoft, Bosch, Uber and so on all have large teams of researchers working in deep learning. AMLAB, QUVA Lab and Delta Lab at IvI are currently very active research labs in deep learning, totaling around 30 researchers in this area.

The new tenure tracker is expected to contribute to fundamental research in deep learning. We anticipate that the field of reinforcement learning combined with deep learning (a.k.a. “deep reinforcement learning”) will drive the next wave of innovation in this field. The tenure tracker is expected to have a keen interest and expertise in this topic.

New deep learning labs at IvI are being planned in collaboration with industrial partners. The new tenure tracker is expected to co-direct these new deep learning labs and co-supervise the PhD students and postdocs that will land in these labs.

The tenure tracker is expected to acquire his/her own independent funding from sources such as NWO/STW (e.g. VIDI), H2020 (e.g. ERC starting grant) and industry.

In terms of teaching, the tenure tracker will contribute to strengthening the curriculum in machine learning and deep learning of the master AI, the Bachelor AI and related programs such as the bachelor at the AUC and the master Information Systems, which all offer ML courses.  The teaching load is around 30%.

The tenure tracker is expected to contribute to valorization, both in terms of engaging with the media as well as applying the state of the art research tools to applications in society. UVA spinoffs that bring technology to industry and society are highly encouraged.

Finally, the tenure tracker is expected to help with the management of the Informatics Institute. 

The candidate’s research interests should fall within the general area of machine learning and in particular deep reinforcement learning. Candidates must hold a doctorate in Computer Science or a related area. A strong track record in research is essential, as demonstrated by independent scientific publications in peer reviewed journals, as well as research interest and expertise in the area of Machine Learning. Candidates should be able to obtain research funding in a competitive environment. In addition, candidates must have a proven track record of teaching abilities as well as the ability to supervise MSc and PhD candidates. At least two years of postdoctoral experience is expected but not strictly necessary. International research experience is also preferred.

The vacancy is for a permanent position according to standard university policies. It is a temporary appointment with a tenure track agreement which will lead to tenured position for a period of 5 years during which the candidate should show his/her competence in science and teaching/education. In the fifth year of the appointment, the tenure decision will be taken. This implies that experienced scientists will be offered a permanent position after a positive assessment and evaluation. The type of appointment is negotiable and depends on age, experience and scientific profile of the selected candidate. The Committee will make an offer pending the vacancy and the experience and track record of the candidate.

The gross monthly salary of the assistant professor position (UD 2/UD 1) will be in accordance with the University regulations for academic personnel and will range from €3,427 up to a maximum of €5,330 (salary scale 11/12), based on a full-time appointment and depending on qualifications and on the number of years of professional experience. The Collective Labour Agreement for Dutch Universities is applicable.

There are also secondary benefits, such as 8% holiday allowance per year and the end of year allowance of 8.3%.

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.

Informatics Institute

The tenure track position at the assistant professor level will be within the Amsterdam Machine Learning Lab (AMLAB) at the Institute for Informatics (IvI), Faculty of Sciences (FNWI), University of Amsterdam (UvA).  AMLAB is led by Prof. Max Welling and also includes Dr. Joris Mooij, Dr. Zeynep Akata and Prof. Ben Kröse as well as around 25 PhD candidates and postdoctoral researchers. Other groups doing related deep learning research within IvI are the Intelligent Sensory Information Systems (ISIS) group (led by Prof. Arnold Smeulders), the Computer Vision (CV) group led by Prof. Theo Gevers, and the Information and Language Systems (ILPS) group (led by Prof. Maarten de Rijke). Prof. Welling also co-directs the Qualcomm-UvA deep learning  & computer vision lab (QUVA) with 12 PhD candidates and postdoctoral researchers and the UvA-Bosch DELTA Lab with 10 PhD candidates and postdoctoral researchers.