PhD Position in Spatiotemporal Neural Networks as Dynamical Systems

University of Amsterdam Informatics Institute


PhD Position Spatiotemporal Neural Networks as Dynamical Systems

Publication date 23 April 2021

Closing date 25 May 2021

Level of education Master's degree

Hours 38 hours per week

Salary indication €2,395 to €3,061 gross per month

Vacancy number 21-282


For the past decades the AI, Machine Learning, and Machine Vision community has primarily focused on analyzing and understanding static data, that is considering only space. However, spatiotemporal data comprise the vast majority of data. Notwithstanding the usual commercial (like YouTube) video recordings and benchmarks, we have seen that:

  • spatiotemporal data are necessary for the next generation of robust and scalable unsupervised and self-supervised learning, or World-based Reinforcement Learning, using time as a ‘label’ in the learning objective;
  • spatiotemporal data are present in all kinds of scientific recordings: satellite spatiotemporal sequences of glaciers, astronomical spatiotemporal measurements of exoplanets, spatiotemporal biomedical recordings of lymphocytic T-cells attacking cancer cells, particle trajectories interacting through space and time, to name a few examples.

Modern deep machine learning and machine vision have trouble when it comes to complex spatiotemporal data, when these are not stylized or span more than a few seconds. The reason is that today’s algorithms are:

  • time extrinsic: (most) algorithms treat time as an extra external dimension or variable, appending it in the input or output channels of the neural network, effectively trying to ‘reverse engineer’ the effect of time on data;
  • time euclidean: (most) algorithms are unstructured machines of finite nature and ‘flat’ geometry, while trying to model complex spatiotemporal recordings that evidently correspond to innumerable spatiotemporal dynamics;
  • time deterministic: (most) algorithms -either explicitly or implicitly- consider the data points as ‘static’ ground truth and repeatable observations, which would occur exactly the same and without any uncertainty if starting from the same initial conditions.


In this PhD position, you will investigate the connection by Spatiotemporal Neural Networks and Dynamical Systems, in an attempt to design novel Time Intrinsic spatiotemporal algorithms. As with dynamical systems, whose state is time-dependent, we want to research neural networks whose filters and parameters, whose features and activations, whose latent variables and hyperparameters, are not static quantities anymore -but functions of time. The aim is to fundamentally reformulate and reinterpret a neural network as an equivalent of a dynamical system. Using tools from dynamical systems, e.g., dynamical systems theory and iterative maps (chaotic attractors; Lyapunov exponent), information theory, phase transitions, we will gain insights into existing neural network structural and functional properties. This, in turn, will help us design novel neural network formulations that are optimal for spatiotemporal data. By learning spatiotemporal neural networks whose computational blocks are functions of time, we could hopefully unlock the true power of unsupervised and self-supervised representation learning using time as ‘label’; as initial evidence implies (Ghodrati, Gavves, Snoek, 2018; Chen et al., 2018). Some overarching questions include:

  • Can we re-interpret neural network layers as time steps, similar to what recurrent neural networks do, while not constrained to finite parameterizations?
  • Can we interpret spatiotemporal sequences as certain dynamical systems?
  • Can we learn spatiotemporal neural networks pertaining certain desirable dynamics?
  • Can we replace standard learning objectives, such as maximum likelihood, with alternatives that relate to optimizing dynamical systems properties (stability, smooth phase transitions, and so on)?
  • Do spatiotemporal neural dynamical systems lead to better representations?

You will be supervised by Dr. E. Gavves, Associate Professor at the University of Amsterdam. This project is financed by the winning H2020 ERC Starting Grant ‘EVA: Expectational Visual Artificial Intelligence’ and NWO VIDI Grant ‘TIMING: Learning Time in Videos’.

What are you going to do

You will carry out research and development in the area of Deep Machine Learning and Vision. The research is embedded in the VISlab group at the University of Amsterdam.

Your tasks will be to:

  • develop new deep machine learning and/or computer vision methods on Spatiotemporal Neural Networks as Dynamical Systems;
  • collaborate with other researchers within the lab;
  • regularly present internally on your progress;
  • regularly present intermediate research at international conferences and workshops, and publish them in proceedings (NeurIPS, IMCL, ICLR, CVPR, ICCV, ECCV) and journals (JMLR, PAMI);
  • assist in relevant teaching activities;
  • complete and defend a PhD thesis within the official contract duration of four years.

What do we require

  • An MSc degree in Artificial Intelligence, (Applied) Mathematics or Physics, Computer Science, Engineering or related field;
  • a strong background/knowledge in machine learning and statistics; computer vision is also a strong plus;
  • a strong background/knowledge in stochastic differential equations, dynamical systems, chaos theory;
  • excellent programming skills preferably in Python;
  • solid mathematics foundations, especially statistics, calculus and linear algebra;
  • a highly motivated, passionate, creative and independent attitude;
  • strong communication, presentation and writing skills and excellent command of English.

Prior publications in relevant machine learning, vision, dynamical systems conferences or journals (NeurIPS, IMCL, ICLR, CVPR, ICCV, ECCV, JMLR, PAMI, IJCV, CVIU) is advantageous

Our offer

A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). This 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 will be €2,395 to €3,061 (scale P) gross per month, based on a fulltime contract (38 hours a week). This is 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 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.

About us

The University of Amsterdam (UvA) is the Netherlands' largest university, offering the widest range of academic programmes. At the UvA, 30,000 students, 6,000 staff members and 3,000 PhD candidates study and work in a diverse range of fields, connected by a culture of curiosity.

Curious about our organisation and attractive fringe benefits such as a generous holiday arrangement and development opportunities? Here you can read more about working at the UvA.

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 position is with Dr. Efstratios Gavves, Associate Professor in the Video & Image Sense lab led by Prof. C. Snoek. VISlab is a world-leading lab on Computer Vision and Machine Learning, and has over 40 PhD students, postdoctoral researchers and faculty members working on a broad variety of core computer vision and core machine learning subjects: from action and object recognition or efficient spatiotemporal deep learning, to stochastic probabilistic models, temporal causality and graph neural networks. In the lab we encourage strongly collaborations. Other labs on Machine Learning and Computer Vision at the Informatics Institute include Amsterdam Machine Learning Lab, led by Prof. M. Welling and  Computer Vision Lab, led by Prof. T. Gevers.


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

  • Efstratios Gavves, Associate Professor, tel. + 31 (0)20 525 8701

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

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. The Informatics Institute strives for a better gender balance in its staff. We therefore strongly encourage women to apply for this position.

Do you recognize yourself in the job profile? Then we look forward to receiving your application by 25 May 2021. You can apply online by using the link below. 

Applications in .pdf should include:

  • CV (max 2 pages) - including a list of publications if applicable and preferred starting date
  • Motivation letter (max 1 page) - motivating your choice for this position
  • Research statement (max 2 pages) – describing your thoughts/ideas about the project, no need for fully-fledged description, a sketch of creative approaches will be appreciated
  • MSc thesis - if still studying, a short summary up to 4 pages is also possible.
  • Record of MSc and BSc courses - including grades and explanation of the grading system
  • Names and contact addresses of two academic references.

Please mention the months (not just years) in your CV when referring to your education and work experience.

We will invite potential candidates for interviews soon after the expiration of the vacancy on 25 May, 2021

In your application, please refer to