PhD candidate in Uncertainty Quantification for Multiscale Computing, 38 hours per week

The Faculty of Science of the Universiteit van Amsterdam (UvA) is one of Europe’s foremost institutions of higher education and research in its chosen fields of specialization. It plays an active role in international science networks and collaborates with universities and industry. The Faculty has approximately 4,000 students and 1,500 staff members spread over four departments and ten research institutes. Each institute has its own research programme, a substantial part of which is externally funded by the Netherlands Organization for Scientific Research (NWO), the Dutch government, the EU and various private enterprises. The Informatics Institute is one of the large research institutes in the faculty, with a focus on complex information systems divided in two broad themes: ‘Computational Systems’ and ‘Intelligent Systems’. The institute has a prominent international standing and is active in a dynamic scientific area, with a strong innovative character and an extensive portfolio of externally funded projects. Project description Background At the frontiers of contemporary science, many if not all of the quantitative research and engineering challenges with high socioeconomic impact are essentially multiscale system problems. A largely unexplored area in multiscale modelling and simulation seems to be that of validation of multiscale models, error propagation, verification, and consistency. Validation and uncertainty quantification, as well as sensitivity analysis of multiscale models should therefore be much better understood and put into practice. We need benchmark results, perhaps stemming from fully resolved models, that can be used to ‘calibrate’ parameters of multiscale models and then, based on uncertainty quantification and sensitivity analyses, seek to put estimates on the quality of predictions that we make with our multiscale models. Uncertainty Quantification, the... read more

Postdoctoral researcher multiscale cell based blood flow modelling and simulation, 38 hours per week

The Informatics Institute of the Faculty of Science is one of the large research institutes in the faculty, with a focus on complex information systems divided in two broad themes: ‘Computational Systems’ and ‘Intelligent Systems’. The institute has a prominent international standing and is active in a dynamic scientific area, with a strong innovative character and an extensive portfolio of externally funded projects. Background multiscale cell based blood flow modelling and simulation Blood is a dense suspension of red blood cells, platelets and white blood cells, with many intriguing properties that are far from understood. We have developed a cell based blood flow model, based on a combination of a Lattice Boltzmann Model for the suspending fluid, an Immersed Boundary Method for the fluid structure interaction, and a boundary element method for the mechanical properties of the individual cells. This model was used to probe deeper into the rheological properties of blood (shear thinning at high densities), as well as details of transport of platelets in non-trivial geometries (such as intracranial aneurisms). Moreover, biological models to account for adhesions and aggregation of platelets were added and used to study the initial processes involved in thrombosis in the context of high shear gradients. Project description You will improve our cell based blood flow model with more advanced modeling of hydrodynamic interactions between LBM-IBM cell membranes, extend to much larger geometries (to be able to simulate 1 mm3 of whole blood), to study in much detail basic transport properties (shear induced diffusion, margination of platelets and white blood cells), to couple this to continuous models of blood flow, thus creating several versions of multiscale models... read more

PhD candidate in Machine Learning for Risk Management in Trading Activities

PhD candidate in Machine Learning for Risk Management in Trading Activities Quantitative Analytics, ING Bank Amsterdam NL Institute for Informatics, University of Amsterdam The Quantitative Analytics group at ING Bank Amsterdam together with the Institute for Informatics at University of Amsterdam invites an application for an Early Stage Researcher (ESR) PhD scholarship in the EC Horizon 2020 funded Marie Skłodowska-Curie Innovative Training Network (ITN) “Training for Big Data in Financial Research and Risk Management.” BigDataFinance The BigDataFinance network provides doctoral training in sophisticated data-driven risk management and research at the crossroads of Finance and Big Data for 13 researchers. The main objectives are (i) to meet an increasing commercial demand for well-trained researchers experienced in both Big Data techniques and Finance and (ii) to develop and implement new quantitative and econometric methods for empirical finance and risk management with large and complex datasets. To achieve the objectives, the emphasis is put on exploiting big data techniques to manage and use datasets that are too large and complex to process with conventional methods. Machine Learning for Risk Management in Trading Activities The main objective of the project is to develop a prototype framework for pricing and risk management using machine learning algorithms and a large variety of heterogeneous and high-volume data, including tick-by-tick quotes of bond prices, market data underlying economic indicators (such as interest rates, foreign exchange rates, inflation rates, and commodity prices) and news feeds. This predictive analytics framework will be used to understand hidden structures in the data and as a test bed for trading risk management with a strong emphasis on back-testing of algorithms in real... read more

Vacancy: Assistant professor ‘Information Processing in Complex Adaptive Systems’, 30,4 to 38 hours per week

This Assistant professor (A/P) position will be within the Computational Science Laboratory (CSL), Informatics Institute, Faculty of Science at the University of Amsterdam. This research driven laboratory concentrates on novel ways to model and simulate the highly complex world around us. CSL consists of a truly multi- and cross disciplinary team of researchers spanning fields as diverse as finance, biology, medicine and computer science. Within the laboratory there is a growing need and interest in the foundations of Complex Adaptive Systems, the chair of Prof. Peter M.A. Sloot. CSL is also responsible for the international (research) Master Computational Science and contributes to various Bachelor and Master programs within the Informatics Institute, the UvA and the Amsterdam University College. Project description The Foundations of Complex Systems wishes to pursue the relatively new concept of information processing as a promising way to develop a theory on dynamic complex systems. This research should shed new light on fundamental features of complex systems, including early detection of tipping points, robustness versus instability, the emergence of complex patterns, and controllability. The young field of complex system science currently lacks a unified framework to study emergent phenomena without recourse to studying mechanistic details of particular complex systems. This complicates the matter and limits the rates of scientific progress. Alternative ways to study these phenomena using traditional system dynamics does not work. The reason for this is that nonlinear and behavioural individual interactions cannot be captured into closed continues models. Nature seems to behave way more ‘algorithmic’ than we ever expected. Information theory has already solved a similar problem in communication science, for which it was... read more

Vacancy: PhD candidate to study stadium crowd behaviour with location analytics

Project description Background Crowd disasters, though rare, have taken many human lives. Yet, controlling crowds is a still unsolved problem. As it takes only a fraction of a minute for a disaster to happen, it is very difficult to respond to it and react preventively. In this project we are going to address this problem, using the Amsterdam Arena stadium as a living laboratory. Based on detection of Wi-Fi and Bluetooth signals from smart phones, we will follow visitors’ locations in real time. We will develop algorithms and software to process locations in real time and to detect ‘abnormal’ behaviour of a crowd that could lead to a disaster. Existing simulation models will be used to model normal and abnormal behaviour in crowds. Based on the results, we will train classifiers to detect abnormal behaviour during a public event. Our ultimate goal is to develop a system that interacts with the crowd in order to prevent escalation of risky situations into actual disasters. Such system will direct (groups of) individuals, e.g. to alternative exits, to minimize congestions during an emergency situation, with mass communication devices like screens or personal devices like smart phones. Requirements Candidates should have completed a master in computer science, or will do so on short term. On a masters level, the candidates must have knowledge of statistics, model building, mathematics and strong coding abilities. Knowledge of networks, cyber security and distributed systems is considered as a pre. The candidates must have the ability to give demonstrations to scientific and business audiences. Their grades list demonstrate of their knowledge and ambition, their master thesis of their ability... read more