PhD Position in Efficient Deep Video and Spatiotemporal Learning
University of Amsterdam Informatics Institute

PhD position in Efficient Deep Video and Spatiotemporal Learning

Publication date 16 March 2021
Closing date 15 April 2021
Level of education Master's degree
Hours 38 hours per week
Salary indication €2,395 to €3,061 gross per month
Vacancy number 21-179

The University of Amsterdam (UvA) is hiring a PhD student in efficient deep video and spatiotemporal learning for the QUVA Lab, a research collaboration between UvA and Qualcomm AI research.

To date, Deep Learning has progressed with leaps and bounds. Most progress has been noted in the image domain, helped by the availability of ever larger datasets, ever stronger GPUs and significant algorithmic advances, e.g. ResNets. Several attempts have been made to generalize to the video -and more generally spatiotemporal- domain, with smaller success, however. A potential issue is that current methods on video can be viewed as deep neural networks originally designed for images (space) and then extended to videos (space-time). However, videos are unlike images. (1) Videos are much more diverse due to their spatiotemporal nature, which implies that relying on larger and larger data is likely to be insufficient. As ResNets showed, excellent sample efficiency is more important than simply larger datasets. (2) Videos present great spatiotemporal redundancy. This redundancy can be either viewed as nuisance which makes spatiotemporal neural networks waste computations cycles and thus must be discarded. Or, this redundancy can be viewed as a feature with which one can learn more discriminative representations. (3) Unlike images, the scale of patterns might extend through time and is not limited by a specific spatial resolution, a problem that exacerbates when moving to the more interesting, longer and more complex spatiotemporal sequences.

To develop deep networks that have been as successful as ResNets in images, we are in need of deep representations that are specifically designed for video and spatiotemporal data so that to have better sample efficiency, better computational efficiency, better modelling efficiency. The research can either have a more theoretical or applied flavor. In this project we will are interested in researching fundamental and novel deep learning algorithms and architectures that address the aforementioned challenges.

What are you going to do?

You are going to carry out AI research and development in efficient deep video and spatiotemporal learning, as part of the QUVA lab at the University of Amsterdam. There will also be regular visits to and interactions with researchers at Qualcomm AI Research, who have an office on campus. At the UvA, you will be supervised by prof. Snoek and/or dr Gavves, as well as an assistant professor we are currently hiring.

Your tasks will be to:

  • develop new computer vision and/or deep machine learning methods on efficient Deep Learning for video and spatiotemporal data;
  • collaborate with other researchers within the lab and Qualcomm AI Research;
  • complete and defend a PhD thesis within the official appointment duration of four years;
  • regularly present internally on your progress and help Qualcomm write patent applications to protect inventions from the lab when requested.
  • regularly present intermediate research results at international conferences and workshops, and publish them in proceedings and journals;
  • assist in relevant teaching activities.

What do we require?

  • A Master’s degree in Artificial Intelligence, Computer Science, or related field;
  • a strong background in computer vision and/or machine learning;
  • excellent programming skills preferably in Python;
  • solid mathematics foundations, especially statistics, calculus and linear algebra;
  • you are highly motivated and creative;
  • strong communication, presentation and writing skills and excellent command of English.

Prior publications in relevant vision and machine learning venues will be advantageous for your application.

Our offer

We offer a temporary contract for 38 hours per week, preferably starting early 2021 for the duration of 48 months. Initial employment is 18 months and after a positive evaluation, the appointment will be extended with 30 months and should lead to a dissertation (PhD thesis). You will get a customized Training and Supervision Plan, which will be evaluated every year.

The salary, depending on relevant experience before the beginning of the employment contract, will be  €2,395 to €3,061 (scale P) gross per month, based on fulltime (38 hours a week), exclusive 8 % holiday allowance and 8.3 end-of-year bonus. A favorable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants. The Collective Labour Agreement of Dutch Universities is applicable.

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


Do you have questions about this vacancy? Or do you want to know more about our organisation? Please contact:

  • Prof. Cees Snoek
    T+31 (0)20 525 7528
  • Dr Efstratios Gavves
    T:+31 (0)20 5258701

About the Faculty of Science and the 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 QUVA Lab is embedded in the Video & Image Sense lab and the Amsterdam Machine Learning lab, two groups within the Informatics Institute working on advanced artificial intelligence. Each project will be done in collaboration with experts from Qualcomm AI Research. The lab is part of the Innovation Center for Artificial Intelligence, a Netherlands initiative focused on joint technology development between academia, industry and government in the area of artificial intelligence.

Job application

The UvA is an equal-opportunity employer. We prioritize diversity and are committed to creating an inclusive environment for everyone. We value a spirit of enquiry and perseverance, provide the space to keep asking questions, and promote a culture of curiosity and creativity.

Do you recognize yourself in the job profile? Then we look forward to receiving your application by 15 April 2021. Please note positions will be filled as soon as an appropriate candidate is found. You may apply online by using the link below.

Applications in .pdf should include:

  • a motivation letter that motivates your choice for this position, and in particular, for which of the research project(s) you are applying;
  • curriculum vitae, including your list of publications if applicable;
  • a research statement on how to approach the project of your choice. Solid and creative ideas will be greatly appreciated. (max 2 pages).
  • a link to your Master’s thesis;
  • a complete record of Bachelor and Master courses (including grades and explanation of grading system);
  • a list of projects and publications you have worked on, with brief descriptions of your contributions, max 2 pages;
  • the names and contact addresses of at least two academic references (please do not include any recommendation letters).

All these items should be grouped into a single PDF file.

The committee does not guarantee that late or incomplete applications will be considered. Please do not send your application to Cees Snoek, Max Welling and/or Efstratios Gavves directly. We will consider only applications via the online process.

Apply now

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