PhD position at the interface of statistical physics, information theory and computer science. 4 years, starting in the Fall of 2021

We are seeking a highly motivated candidate to work on the use of classical spin models for high order pattern detection and information coding in binary data. In this context, recent studies have highlighted the existence of linear transformations (called gauge transformations) that map a model into another mathematically equivalent model. The group is interested in understanding how to use these transformations for data analysis.

The group is shared between the Institute for Theoretical Physics (ITFA) in the Institute of Physics (IoP) and the Informatics Institute (IvI). The successful applicant will be embedded in a stimulating multi-disciplinary research environment.

Selection will start July 31 and continue until the position is filled.
For further details and application form:

For questions, please contact Clelia de Mulatier at

Open position: PhD candidate on In-Silico Modelling of late Thrombosis on medical implants

We are seeking a highly motivated PhD candidate to work on the in-silico modelling of thrombosis, applied to late thrombosis on medical implants, focusing on coronary artery stents.

For further details and the application form please follow the link:

Two interdisciplinary Postdoc (3y) positions with DIEP@UvA

DIEP@UvA has several open positions consisting of three year DIEP postdoc fellowships. DIEP@UvA seeks senior postdocs whose research interests align with those of at least two of the following institutes: Institute of Physics (IoP), the Korteweg-de Vries Institute for Mathematics (KdVI), the Institute for Logic, Language and Computation (ILLC), the Informatics Institute (IvI), the Van ‘t Hoff Institute for Molecular Sciences (HIMS) and the Institute for Advanced Study (IAS).

You will join an exciting team of scientists and conduct exciting interdisciplinary research within emergence in areas such as non-equilibrium systems, causality, collective intelligence, network theory, multiscale modelling, quantum information, phases of mater, etc (for a more detailed description of DIEP@UvA research priority areas).

DIEP@UvA has multiple ongoing research projects in: multiscale modelling of systems out of equilibrium; rare events in e.g. climate science; models of causal inference; logic for multi-agent systems; quantum and classical information theory; self-organisation in active matter; emergent behaviour in catalytic processes; emergence of collective social behavior; emergent phases of matter and spacetime; etc. For concrete ongoing research projects within DIEP@UvA check DIEP@UvA’s research portfolio.

For more details and applying click here.

PhD/Postdoc position: Networks and value chains in organized crime

We are seeking a researcher (PhD candidate or Postdoctoral researcher) for a computational network science position to work on an exciting research project in an interdisciplinary team. You will focus on the use of mathematical and computational methods to study the organizing principles and adaptative, bottom-up nature of the various types of networks and value chains underlying modern organized crime activities.

There is a growing consensus that the complexities underlying crimes and criminal organisations cannot be unravelled by traditional methods alone. A shift in research paradigm to complex adaptive systems and network thinking is therefore imperative to move this field forward. Organized crime is a complex interplay between social networks, financial networks, communication networks, trust, opportunity, among others. A complex systems approach that studies these pathways, how these pathways adapt, and their interactions can support analysts and investigators in effectively tackling undermining criminal activities in a strategic manner.

Particularly novel in this project is that multiple rich intelligence datasets (anonymised) will be combined in order to create large, multiplex networks surrounding criminal activities. This quantitative data will be combined with qualitative knowledge from domain experts. The resulting networks (and value chains) will be conceptualized as a dynamical system which are adaptive and decentralised. The goal is to model, mathematically and computationally, the process of formation and evolution of the networks and value chains therein, and subsequently to use complexity science concepts to study the function of the emergent network topology as a resilient, bottom-up infrastructure for information, money, and commodities.

For more details please click here.

PhD candidate on Neural Networks as Dynamical Systems

This Ph.D. position is part of a cross-theme project within the Institute of Informatics.

We seek a multidisciplinary researcher who can study deep neural networks in the context of complex adaptive systems analysis. Specifically, the aim is to reformulate and reinterpret a neural network description as an equivalent description of a dynamical system. Subsequently, using tools from dynamical systems and complex systems you will analyze the dynamical system and thereby gain insights into the original neural network. The goal is to gain insights into the structural and functional properties of the neural network computational graph resulting from the learning process. Techniques that will be employed include dynamical systems theory and iterative maps (chaotic attractors; Lyapunov exponent), information theory (Shannon entropy, mutual information, multivariate measures such as synergistic information (Quax et al., Entropy, 2018)), and network theory.

For more details and how to apply please click here.

PhD position on the intersection of deep learning, causality, and information theory

This Ph.D. position is part of a cross-theme project within the Institute of Informatics.

We seek a multi-disciplinary researcher who can bring advanced information-theoretic concepts, in particular synergistic information, from the field of complex adaptive systems into the field of deep learning and causal inference. The focus of this PhD project is on adapting and further developing the theory of synergy with the goal of making it viable for optimization. The end goal is to train deep representations that are robust and meaningful for un-/semi-supervised learning, transfer learning, and causal inference. For these purposes we anticipate that a ‘synergistic bottleneck principle’ needs to be formulated, in analogy to the ‘information bottleneck principle’. It should be worked out in terms of variational methods and applied to benchmark data sets. Furthermore, the question will be explored of how the optimization guided by synergistic information may serendipitously lead to causal representations.

This PhD project is a close collaboration between the Computational Science Lab (CSL, promotor prof. Peter Sloot) and the Amsterdam Machine Learning Lab (AMLAB, promotor prof. Max Welling).

For more details and how to apply please click here.

Vacancy: Tenure Track

Vacancy: Tenure Track

We are seeking a highly qualified assistant professor who is interested in interdisciplinary research, with exceptional computational and modelling skills and proven expertise in data driven system dynamics. The focus will be on new ways to integrate data from – and conceptual models of- dynamic complex systems into predictive System Dynamics (SD) models. These SD models will be used to explore the outcome of ‘what if’ interventions. The goal is to study novel ways to generate and validate conceptual and computational system dynamics models of dynamic complex systems that cover multiple scales and integrate data and knowledge from multiple domains (e.g. sciences, social science, psychology, etc.). The resulting models will then be applied for deeper understanding of those systems, and predicting their response to interventions. Crucial is the notion that System dynamics requires large scale distributed computing of multi-scale processes where the data can be updated in real-time and the SD model needs to learn and adapt accordingly.


For more details and submitting your application, please go here.

New open position for a PostDoc in In-Silico Stroke Trials

New open position for a PostDoc in In-Silico Stroke Trials

Are you a high potential young researcher who recently obtained a PhD degree in computational biomedicine or related fields, and do you want to join our multidisciplinary team to further develop your career as an independent scientist? Do you want to push the frontier of computational modelling in medicine, and help shape the exciting new development of in silico trials? Are you keen to join our international project that aims to develop, validate, and apply the first in silico stroke trial? If you recognise yourself, we are happy to invite you to apply for this position.

Project description

An exciting new emerging application of Computational Biomedicine are in-silico trials, which aim to reduce, refine, or even replace animal studies or (pre-) clinical human trials by simulating medical products or treatments on the population level.

In the INSIST project we aim to develop in-silico trials for acute ischemic stroke.

Your role will be to integrate models, as developed within the INSIST project, for virtual stroke populations, brain perfusion and metabolism, stroke treatment options (mechanical thrombectomy and thrombolysis), and statistical clinical outcome models into an overall in-silico stroke trial, to validate it on retrospective data from earlier stroke trials, and in collaboration with medical professionals and medical industry, to design and carry out two prospective in-silico stroke trials.

For application please follow this link:–-in-silico-stroke-trials.html

New open position for a PostDoc in In-Silico Stroke Trials

Two open positions at our lab: PhD, Scientific programmer

We (the Computational Science Laboratory) are looking for a PhD candidate and a Scientific Programmer to join our team. Please follow the provided links for further details.

PhD in blood flow simulations

What are you going to do?

Your main objective will be to develop, validate, and apply a new computational model to simulate the flow of red blood cells, platelets, and other components under specific disease and medical device related conditions. You will build on the Multiscale Modelling and Simulation Framework and develop your model within HemoCell to realize an efficient, high performance computational method that can provide answers to complex biological questions in the context of human vascular diseases, such as thrombosis, hemostasis. You will cooperate with external experimental groups throughout the process.

You will:

  • develop a new multiscale computational model within HemoCell, adding the molecular level, adding biochemistry, and adding platelet adhesion and aggregation, and apply it in the context of human vascular diseases;
  • complete and defend a PhD thesis within the official appointment duration of four years;
  • collaborate with other researchers within our group and in external groups;
  • regularly present intermediate research results at international conferences and workshops, and publish them in proceedings and journals;
  • assist in relevant teaching activities.

Scientific Programmer Computational Biomedicine

What are you going to do?

The main focus will be on the development and maintenance of our software portfolio (for an example see our open-source cellular flow modelling toolkit Hemocell and to contribute to workflows in relation to in-silico stroke trials (see INSIST). You will work, together with PhD students and Postdocs, on new, specialized applications of our software, as well as on improving the performance of our software. These applications are often embedded in large international projects in cooperation with external partners in e.g. Sheffield, London, or Geneva. You will support our scientific team to realize efficient HPC simulation solutions with these codes.

This position can give grounds to fast professional development in parallel numerical techniques, simulation methods, and application of state-of-the-art computational solutions for large-scale systems, and High Performance Computing on Europe’s largest supercomputers.

Two open PhD positions in computational network science

Two open PhD positions in computational network science

We are seeking two PhD candidates for a computational network science position to work on an exciting research project in an interdisciplinary team. They will focus on the use of mathematical and computational methods to understand social and financial processes in crime and their interplay.

PhD in network information dynamics in criminal networks

You will focus on analysing flow dynamics in (multiplex) networks. You will use information theory and network theory to identify the optimal information positions as well as driver positions (causal influence). The flows of information, goods, money that flow through the system lead to emerging patterns of complex criminal network organisation and behaviour. When does the network reach a tipping point if typical information is transferred through the network? How is money transferred through the network and is this different from the flow of information? As a starting point for inspiration please see the referenced article*.

In the present context a good information position of an individual is to possess information about (or be correlated with) a large number of other individuals, possess information which stays relevant for a long period of time, or both. In addition to the flow of information or causality we are also interested in other flow dynamics, such as money, commodities, or trust.

* Quax, Rick, Andrea Apolloni, and Peter MA Sloot. ; The diminishing role of hubs in dynamical processes on complex networks.’ Journal of The Royal Society Interface 10.88 (2013): 20130568.

Further details:

PhD in adaption in criminal networks

You will focus on identifying and modelling the dynamics of adaptation to different intervention strategies by law enforcement. Criminal networks are infamous for their resilience against different intervention strategies. Interventions can be aimed at central actors, but also on actors with a specific role within a value chain. Each criminal activity, such as cocaine trafficking, money laundering or migrant smuggling, has its own value chain. For example, illegal cannabis cultivation involves property owners, electricians, cutters, distributers, and sellers, each with specific skill sets and dependence on each other. Read further about the subject in this article*.

The first goal is to understand the adaptation processes after different types of interventions based on historic police data. Second, is to integrate these insights into a model for adaptation that can be used to simulate the different effects of interventions, such as centrality attack, value chain attack, etc. Third, we aim to use the knowledge of adaptation processes and value chains to predict potentially missing links from the inherently incomplete data sets, by matching partial value chains and inferring highly likely unobserved links which would complete the value chain.

*Duijn, Paul AC, Victor Kashirin, and Peter MA Sloot. ‘The relative ineffectiveness of criminal network disruption.’ Scientific reports 4 (2014): 4238.

Further details: