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Training

Projects

As part of their application to the DSSC COFUND Research and Training Programme applicants are invited to choose from among the following DSSC projects the one or several that would become their PhD project, should they be selected. Once admitted to the Programme, the PhD candidate’s progress on the research project will be monitored by a Supervisory Team.

Abbreviations:

  • Johann Bernoulli Institute for Mathematics and Computer Science (JBI)
  • Artificial Intelligence and Cognitive Engineering (ALICE)
  • Engineering and Technology Institute Groningen (ENTEG)
  • Kapteyn Astronomical Institute (Kapteyn)
  • Zernike Institute for Advanced Materials (ZIAM)
  • Netherlands Institute for Space Research (SRON)
  • Netherlands Institute for Radio Astronomy (ASTRON)


ADAPTIVE MODELS & BIG DATA

Advanced models & Big data
Advanced models & Big data
Project 1: Nonlinear time series analysis

A time series is a sequence of data points indexed in time order. Typical examples of time series are heights of ocean tides, counts of sunspots, or the daily closing value of the Dow Jones Industrial Average. Analysis of time series is important in forecasting applications. In this project, the PhD student will study nonlinear time series analysis and reconstruct the underlying dynamical system from a sequence of observations. The main challenges are to extend this approach to deal with big data and high dimensionality, and to obtain data-driven adaptive models for non-stationary systems that are continuously monitored. Specific questions are how to model the tails of distributions to distinguish between outliers (extreme events) and noise, and to develop tools using big data sets to understand the dynamics and mechanisms leading to rare but important events in complex dynamical systems. Applications of this project include extreme events in dynamical systems, such as weather, climate, or ecological systems (e.g. "tipping points").

Keywords: Nonlinear time series analysis, Extreme events, Complex systems.

Field of expertise involved: Mathematics

Supervisory team:

Institutes involved:
JBI
ENTEG

Potential partners:

  • Centre for Doctoral Training in Mathematics for Real-World Systems
  • Royal Netherlands Meteorological Institute (KNMI)
Project 2: Dimensionality Reduction for Huge Classification Problems
This project focuses on the huge classification problems with thousands of classes and millions of observation instances, which are at the center of current research in pattern recognition and machine learning. Little is a priori known about the difficulty or complexity of classification problems and researchers often stay within the narrow range of allowable methods which are mostly used in their confined and specific research culture. In this project, we will address pattern classification problems coming from four disciplines: 1) astronomy: star classification; 2) gene-expression matrices: Affymetrix-based tissue classification; 3) finding ligands in catalysis: Raman spectroscopy; and 4) image classification. With these four disciplines, we can cover a wide range of classification problems in terms of: a) number of instances, b) number of classes, and c) dimensionality of the data, from a few hundred to tens of thousands of input features. The goal is to identify formal guidelines for dimensionality reduction, based on the intrinsic geometric complexity of the data problem. Currently, many different dimensionality reduction techniques exist ranging from principal component analysis to deep autoencoders and many possible embeddings of the examples in lower-dimensional manifolds.  By shedding light on their optimal usage for the considered problems, this project pushes machine learning a big step further for solving real-world classification problems.  This project runs parallel to a related H2020 project in industrial predictive maintenance (1 PhD) and two Dutch science foundation projects (NWO) in pattern recognition/machine learning. Data will be provided by partners in Kapteyn, ZIAM, GELIFES/UMCG.

Supervisory team:

Institutes involved:
JBI
ALICE

Potential partners:

  • Kapteyn Astronomical Institute
  • The Zernike Institute for Advanced Materials
  • Groningen Institute for Evolutionary Life Sciences
  • University Medical Center Groningen
Project 3: Low-complexity, parallel and distributed algorithms to detect and classify objects in large infrared, hyper-spectral and 3D sensor images

Project 3 (JBI, Kapteyn, ALICE - DELETE ALICE FROM THIS ENUMERATION) will develop low-complexity, parallel and distributed algorithms that robustly detect and classify objects in large infrared, hyper-spectral and 3D sensor images. The methodology will be based on computer vision and machine learning techniques, such as trainable morphological image processing filters which can be efficiently implemented using sequential and parallel implementations based on the max-tree or related alpha-tree data structure. We intend to explore two ways of combining these morphological scale-space data structures with machine learning, i.e. 1) using machine learning to classify nodes in the trees based on so-called vector-attribute filtering, and, 2) feeding key-point features detected by analysis of these scale spaces to deep convolutional networks, after automatic rescaling and rotation to a standard scale and orientation. This would allow scale-invariant analysis of huge images using deep learning, without excessive compute power requirements. The methods will be applied to several use cases: 1) the detection of buildings at many different types of resolution from remote-sensing data, as needed in the Global Human Settlement Layer project which aims to improve understanding of urbanisation, to assist urban planning, and 2) to support disaster relief, 3) for detection and analysis of objects in large astronomical surveys. This subproject will profit from existing collaborations of JBI with the Joint Research Centre (JRC) in Ispra, Italy. Potentially, a fourth use case on large electron micrographs obtained from the UMCG could be included.

Keywords: Scale spaces, connected filters, machine learning, remote sensing, astronomical surveys

Supervisory team:

Institutes involved:
JBI
Kapteyn

Potential partners:

  • ASTRON
  • SRON
Project 4: The Value of Data

Data is a driver of value creation. For instance, the value of many tech companies is based on data-guided marketing algorithms. Businesses optimize the gains of their processes using data, e.g. in fraud detection. For a good understanding of the value of data, three theoretical domains are relevant: probability theory, expected utility theory and logic. Probability theory provides the foundations for descriptive statistics, expected utility theory models subjective values, and logic represents complex qualitative relations. In the project, argumentation-based formal methods and algorithms will be developed connecting probability theory, expected utility theory and logic. The project will extend techniques developed for evidential reasoning about the facts to practical reasoning about actions.

Keywords: Artificial intelligence, argumentation, decision making, probability theory, data analysis.

Fields of expertise involved: Artificial intelligence, Argumentation theory, Decision theory, Probability theory, Data analytics.

Supervisory team:

Institutes involved:
JBI
ALICE

Project 5: Topological Analysis Methods for Big Data

This project is concerned with topological analysis methods for big data, and the insights this provides into the emergence of complexity in dynamical systems. The PhD student who will select this project will develop and use topological data analysis methods for the analysis of big data originating in cosmological and astronomical studies. It will be based on recent developments that have shown that state-of-the-art topological data analysis (TDA) methods, such as persistent homology, uncover new understanding and insights into the multiscale topological description of the Megaparsec weblike cosmic matter distribution. Betti numbers and topological persistence turn out to represent powerful means of describing the rich connectivity structure of the cosmic web and of its multiscale arrangement of matter and galaxies. This has shown that topological data analysis methods provide provide new means of understanding the shape of data and uncovering hidden patterns and relations. The PhD student will focus on using TDA to detect structure in data coming from large scale cosmological simulations and large cosmological redshift surveys, and use these to look into the homological properties of the observed Cosmic Web and study how these properties change in time. Intimately related to the singularity structure of the mass distribution represented in the data, the project should also enable the study of the connection with the dynamical evolution of the probed system. By means of sophisticated new reconstruction methods producing maps of the evolving mass distribution in the local Universe, the statistical and topological data analysis of real observational data may thus lead to a unique means of studying the phase-space dynamics and singularity structure of the cosmic web. This will yield unique insights in the dynamics of the cosmic web and culminate in a dynamical characterization of the observed distribution of galaxies in upcoming large galaxy surveys such as Euclid and SKA. The potential for additional applications is large, such as the issue of astronomical object classification in data coming from astronomical instruments. In this context, the PhD student will interact with other COFUND PhD students who need advanced methods for understanding the shape of their data.

Keywords: Topological data analysis, Cosmology, Astronomy, Big Data

Fields of expertise involved: Algebraic Topology, Computational Geometry, Cosmology, Astronomy

Supervisory team:

Institutes involved:
JBI
Kapteyn

Potential partners:

  • CIT
Project 6: A Visual Analytics Approach of Big Data

Project 6 focuses on a Visual Analytics approach of big data, that is, combining automated data analysis techniques with interactive visual interfaces to pose, refine, and confirm hypotheses about complex phenomena represented by such data.

We will focus on high-dimensional time-dependent data, that is, large sets of observations having each many measurement values that represent the evolution of a phenomenon over time. Challenges are here the large numbers of observations (millions or more); dimensions (hundreds or more); and time steps (thousands or more). Finding efficient and effective ways to discover and display complex patterns in this very high dimensional data space is the key challenge in modern (visual) data exploration.

To solve this, we will develop methods for data-size and data-dimensionality reduction; automatic and user-assisted discovery of meaningful patterns hidden in the data; and intuitive visual depiction of such patterns,  their evolution, and their inter-relationships. For this, we will develop new techniques for pattern mining, dimensionality reduction, scalable information visualization, uncertainty visualization, relational visualization, and interactive data querying. We will achieve scalability for big data by using CPU and GPU parallelization, and multiscale data-representation and visualization techniques.

We will apply our interactive visual analytics pipeline to two use cases: 1) large simulations or observation catalogs in an astronomical pilot project to address questions on galaxy evolution; 2) prediction of neurodegenerative diseases from multi-centre clinical brain data.

This PhD student follows up on a recently completed project on e-Visualisation of Big Data funded by the Dutch e-Science Centre NLeSC.


Keywords: Visual analytics, high-dimensional data visualization, interactive pattern discovery, scalable information visualization, astronomical data, medical data.

Fields of expertise involved: Multidimensional data analysis, Data and pattern mining, Interactive visual analytics and information visualization, Disease prediction from medical data, Galaxy evolution.

Supervisory team:

Institutes involved:
JBI
Kapteyn

Potential partners:

  • Philips
Project 7: Understanding the complex system of the brain

Project 7 engages the issue of Big Data and neuroscience. It aims to understand the complex system of the brain by bringing together the expertise of ALICE in modeling large-scale cognitive systems and relating those models to neural data with the expertise of JBI in visualizing large-scale neural structures and processing large volumes of data. The work of the ESR/ESRs embedded in this team will help to understand the functional role of the connectome—the connections between brain areas and their interactions. A particular topic of interest is how connections between brain areas implement cognitive control, and how that could reduce neural noise such as distraction and mind-wandering. Better understanding the functioning of these brain networks could potentially lead to applications that could predict whether a person is distracted or exerting cognitive control on a single-trial level.

The required large-scale data will be generated by the new Wellcome Trust-funded neuroscience centre at Cardiff University (CUBRIC). The connectome of these data will be analyzed with methods developed by ENTEG (primarily behavior of brain oscillations), visualized by JBI, and subsequently, different cognitive processes will be related to the connectome by ALICE’s cognitive modeling group. The insights gained from will make the team involved in this project line one of the leading groups in model-based neuroscience in Europe. In this highly multidisciplinary project, the ESR will collaborate with Project lines 1, 2 and 12.

Keywords project: Neuroscience, Connectome, Cognitive models, Mind-wandering, Brain oscillations

Fields of expertise involved: Neuroscience, Visualization, Mind-wandering, Neuroimaging

Supervisory team

Institutes involved:

ALICE
JBI
ENTEG

Potential Partners:
  • University of Cardiff (CUBRIC)
Project 8: Interplay of data science inference methodologies and network models

Networks are an important modelling paradigm for many current questions, such as (i) infectious disease spreading, (ii) financial risk in the global financial network, (iii) genetic disease and network pharmacology networks, (iv) gossip and other viral events in big social networks. One of the big questions in all of these is what drives the network process. Whereas models for temporal evolution of networks and models for correlated networks are available, the statistical-interferential aspects of these models are at the cutting edge of statistical development.

This project will use various sparse inferential techniques as a useful paradigm for inference in the presence of system complexity. An important dynamic network model is the stochastic actor-oriented model, which is able to describe the temporal co-development of social networks and behaviour in the presence of social selection (where network changes depend on network as well as behaviour) together with social influence (where behaviour changes depend on behaviour as well as network). However, the applicability of this model is limited to the small networks and a small number of driving behavioral factors. The aim of this project is to extend the method to large networks with a large number of potential factors.

In this work we will build forth on our expertise in sparse inference of dynamical graphical models and of static social networks. This is a collaboration between the statistics group in the mathematics department and the argumentation group in artificial intelligence department.

Keywords: Statistics, Networks, Sparse inference, Stochastic differential equations

Supervisory team:

Institutes involved:
JBI
ALICE

Potential partners:

  • Rijksdienst voor het Wegverkeer (RDW)


COMPLEX SYSTEMS & ENGINEERING

Complex Systems & Engineering
Complex Systems & Engineering
Project 9: Distributed methods to handle smart power microgrids

Microgrids, which are reduced size power networks that can operate independently from the main power network, have been advocated as the most promising technological solution to facilitate the penetration of renewable energies in the power market. Project 9 will focus on the modelling, control and optimisation of microgrids, proposing distributed control algorithms that can preserve the stability of the microgrid in spite of the large uncertainty in the system due to the erratic nature of renewable power sources. The control and optimisation of these systems will be tackled using energy functions inspired by the physics of the system as the main analytical tool. The use of these energy functions will allow us to investigate AC, DC as well as hybrid AC/DC microgrids in a unified framework. The envisioned algorithms will make use of a communication layer to coordinate fair power sharing among the different sources and achieve active load regulation, and the resulting truly cyber-physical system will be again analysed within the energy framework to be developed.

Keywords: Microgrids, Distributed Control Algorithms, Optimization, Nonlinear Systems, Complex Oscillatory Networks.

Fields of expertise:
Control Theory, Optimization, Dynamical Systems, Power Engineering, Network Science.

Supervisory team:

Institutes involved:
JBI
ENTEG

Potential partners:
  • MathSys
  • DNV-GL
Project 10: Distributed control methods under communication constraints with applications to sensor networks in a smart industry setting

In modern industry, there is an increasing interest in control systems employing multiple sensors and actuators, possibly geographically distributed. Communication is an important component of these networked control systems. Understanding the interactions between control and communication components is especially important to develop systems that possess scalability features. Existing solutions for distributed control have little to no scalability features in terms of both convergence time and accuracy caused by limited bandwidth and quantization issues. The ESRs who select this Project line will develop novel distributed control methods, with emphasis on event-based and self-triggered communication protocols. A specific case study will be considered in terms of distributed “average” and “max-min” consensus networks, which are prototypical networks for distributed sensing and actuation.

Keywords: Sensor Networks, Nonlinear consensus, Hybrid Systems, Self-triggered Algorithms, Coordination Control.

Fields of expertise: Control Theory, Dynamical Systems, Signal Processing, Computer Science, Network Science.

Supervisory team:

Institutes involved:
ENTEG
JBI

Potential partners:

  • MathSys
Project 11: Cyber security with applications to both smart energy systems and smart industry

Owing to advances in computing and communication technologies, recent years have witnessed a growing interest towards Cyber-physical Systems (CPSs), i.e., systems where physical processes are monitored/controlled via embedded computers and networks. The concept of CPSs is extremely appealing for smart energy systems and smart industry, but it raises many theoretical and practical challenges. In particular, CPSs have triggered the attention towards networked control in the presence of cyber-attacks. In fact, unlike general-purpose computing systems where attacks limit their impact to the cyber realm, attacks to CPSs can impact the physical world as well. The ESRs who select this Project line will develop novel monitoring and control systems that are resilient against Bias Injection (BI) and Denial-of-Service (DoS) attacks. Two specific case studies will be considered: (i) BI attacks on distributed consensus networks for environmental monitoring, and (ii) DoS attacks on distributed control algorithms for optimal frequency regulation in smart grids.

Keywords: Cyberphysical Systems, Networked Control Systems,  Hybrid Systems, Power Networks, Smart Industries.

Fields of expertise: Control Theory, Dynamical Systems, Cybersecurity, Computer Science, Network Science.

Supervisory team:

Institutes involved:
ENTEG
JBI

Potential partners:

  • MathSys
Project 12:  Stochastic Second Order Oscillator Networks

This project focuses on Stochastic Second Order Oscillator Networks, and specifically on the statistical properties of second order oscillator networks with stochastic components. Such systems model power distribution networks or neural networks and their properties are not as well understood as those of first order networks. More specifically, the ESR involved in this project will focus on Non-Equilibrium Stationary States (NESS). The ESR will also study networks that are constructed stochastically (and possibly also evolve according to some stochastic rule), with focus on phenomena such as phase transitions, synchronization, and self-organization. This provides a rich case study of random processes which take place on random networks, a topic in the frontier of network science.

Keywords: Oscillator Networks, Non-equilibrium stationary states, Random networks, Random processes.

Fields of expertise: Dynamical Systems, Statistical Mechanics, Network Dynamics.

Supervisory team:

Institutes involved:
JBI
ENTEG

Potential partners:

  • Cardiff University Brain Research Imaging CentreCenter for Medical Imaging North-East Netherlands.
  • Det Norske Veritas - Germanischer Lloyd.
  • The Center for Doctoral Training in Mathematics for Real World Systems.
Project 13: Home robotics

Robots must be able to handle unforeseen circumstances. Neither knowledge representation nor machine learning approaches allow for the sufficiently robust handling of unforeseen circumstances. As a result, new hybrid technology must be developed that combines knowledge technology for the manual representation of behavior-guiding scenarios for new and exceptional circumstances with data technology to evaluate and adapt these scenarios. In the robot architecture developed in the project, a hypothesis testing cycle will be modeled using argumentation-based techniques designed for the combination of logic-based scenario representations and probability-based data analysis. The architecture will be tested in the international annual RoboCup@Home competition.

Keywords: Artificial intelligence, argumentation, robotics, scenario modeling, human-machine interaction.

Fields of expertise involved: Artificial intelligence, argumentation theory, robotics, human-machine interaction.

Supervisory team:

Institutes involved:
ALICE
ENTEG

Project 14: Nested Dynamical Systems with Increasing Complexity

Continuum models in science and engineering are often formulated as partial differential equations (PDEs). A prime example is the Navier-Stokes equation which describes the dynamics of fluids on many different time and spatial scales. For example, the equation can be used to model the weather, ocean currents, water flow in a pipe, and air flow around a wing. For simulation purposes discretisation methods, such as finite-difference methods or Galerkin-like projections, are used to reduce a PDE to a system of finitely many ordinary differential equations. Increasing the resolution of the discretisation gives a family of nested systems with increasing complexity. The key question is which dynamical and statistical properties persist within this family. Answers to this question may result in novel model reduction strategies for complex, nonlinear systems. The potential applications of this project are broad: they may include models for fluid dynamics (e.g. turbulence) and control systems.

Keywords: Continuum models, Complex systems, Model reduction.

Fields of expertise involved: Mathematics

Supervisory team:

Institutes involved:
JBI
ENTEG

Potential partners:
  • Centre for Doctoral Training in Mathematics for Real-World Systems
Project 15: Tailor-made model reduction methods for integrated energy systems
The rapid increase of renewable/sustainable energy production leads to daunting challenges in the design, analysis, and control of the modern energy system. Namely, the large number and decentralized nature of renewable energy sources calls for a change of the current paradigm in which energy production is largely centralized in a small number of power plants.

Instead, future energy systems will constitute a network of a large number of heterogeneous  dynamical systems, corresponding to a variety of agents such as large power plants, wind farms, solar collectors, industrial and household consumers.

One major challenge in the design of these highly integrated energy systems is that the complexity of the resulting mathematical models is immense, such that the analysis, simulation, and controller design become intractable. Therefore, satisfactory methods for the approximation of such complex models by lower-order, less complex models are needed. In order for these low-complexity models to be reliably employed as substitute of the original model, they should not only accurately preserve the behavior of the original model, also inherit/preserve/reflect structural properties of the original model, in particular the underlying physical network structure. Consequently, this project will investigate network-structure preserving model reduction methods as well as a priori error bounds for such methods.

Keywords: Networks of dynamical systems,  structure preserving model reduction, smart energy systems, model reduction for networks, graph simplification

Fields of expertise involved: Systems and control engineering, Feedback control theory, Mathematical modelling, Model reduction, Graph theory.

Supervisory team:

Institutes involved:
JBI
ENTEG

Potential Partners:

  • Centre for Doctoral Training in Mathematics for Real-World Systems (MathSys CDT)
  • Philips
  • Centre for information technology (CIT)
  • Fraunhofer
  • DNV-GL

ADVANCED INSTRUMENTATION & BIG DATA

Advanced Instrumentation & Big Data
Advanced Instrumentation & Big Data
Project 16: Modeling and control of hysteretic deformable mirrors for high-contrast imaging systems

In this project, we will develop model-based nonlinear control algorithm for the control of a novel hysteretic deformable mirror (HDM). For enabling wavefront control in a high-contrast coronagraph instrument for future space-telescopes, with the ultimate goal of finding and characterizing Earth-like exoplanets, we have developed a novel deformable mirror (DM) concept based on new hysteretic piezoelectric material and new distributed polarization method. It allows for a high-density, low-power and scalable DM system which is crucial for space application. The demonstrator is currently being built and tested by a multidisciplinary team from ENTEG (Engineering and Technology institute Groningen), ZIAM (Zernike Institute for Advanced Material), SRON (Dutch Space Research Agency) and KAI (Kapteyn Astronomical Institute).

As an instrumental element of this novel DM system, the smart control system will be developed by the PhD student/Early Stage Researcher (PhD student) within DSSC. In particular, the PhD student will (i). model and characterize the realized HDM demonstrator, including, hysteresis and dynamical behavior; (ii). perform systems identification/identify parameters based on the real-time dynamical data; (iii). develop a distributed control method for achieving desired shape of the DM; and (iv). develop a combination of data-driven and model-based distributed control algorithm for continuous and iterative improvement of the control systems.

Keywords: Control algorithm; Distributed nonlinear control method; Advanced instrumentation; Mechatronic systems; Systems identification; Model-based control algorithm; Data-driven control algorithm.

Field of expertise: Systems and Control; Applied Mathematics; Applied Physics; Astronomy; Mechanical Engineering; Electrical & Electronics Engineering; Mechatronics; Advanced Instrumentation; Opto-mechatronics; Precision Engineering.

Supervisory team:

Institutes involved:
ENTEG
Kapteyn
ZIAM
Netherlands Institute for Space Research

Potential partners:

  • SRON
Project 17: Scalable algorithms to process massive datasets from radio astronomy

One of the holy grails in cosmology is the discovery of neutral hydrogen in the infant Universe (its first billion years), which can tell astronomers and cosmologists how the first stars, galaxies and black holes formed, how dark matter evolved and whether gravity indeed follows Einstein’s general relativity. The low-frequency part of the Square Kilometer Array (SKA) –  the world’s largest radio telescope currently under construction in Australia and South Africa –  can not only discover the emission of this hydrogen, but it can even make images of its distribution and how it evolves in time (redshift). The data volume of the SKA, however, is huge (1000 Tb/day) and will require novel scalable algorithms in order to process the total amount of data accumulated in such a project (106 Tb or 1 Exabyte).

The PhD student project will focus on how subtle effects in the processing (i.e. error-correction and imaging) of these data can be uncovered using machine learning techniques, such as neural networks and pattern recognition. Such an approach is becoming necessary in order to quickly explore the very high-dimensional data-set, beyond any human ability, and discover effects and relations in the data that will enable finding these extremely faint signals of neutral hydrogen from the infant Universe.

Keywords: Cosmology, Radio Telescopes, Machine Learning, Infant Universe.

Fields of expertise involved: Cosmic Dawn & Reionization, Radio astronomy, Machine Learning, Signal Processing.

Supervisory team:

Institutes involved:
Kapteyn
JBI

Potential partners:

  • ASTRON
  • CIT
Project 18: Methods for automated and robust analysis of astronomical data

Project 18 (Kapteyn, JBI and ASTRON) will focus on methods for automated and robust analysis of astronomical data taken with the Low Frequency Array (LOFAR). The Early Stage Researcher will develop sophisticated calibration methods that connect astronomy and complex data systems, as part of a project on Advanced Instrumentation and Big Data. They will use novel computing science methods to analyse data from the LOFAR radio interferometer, built and operated by ASTRON. LOFAR is the world’s largest connected radio telescope that is optimised to operate a low radio frequencies at arc-second angular resolution. The student will develop new techniques for the automated and robust analysis of the data taken with the International LOFAR Telescope, which includes signals from the stations both within the Netherlands and throughout Europe. This data stream will be used to investigate the low energy radiation from supermassive black holes at the highest possible angular resolution to investigate particle acceleration and energetics in the Universe’s largest natural particle accelerators. This will be done by combining the resolving power of LOFAR and the added magnification provided by gravitational lensing to study black hole physics at redshifts where the active phase from such objects is expected to peak, and have the largest influence on the build-up of the stellar host galaxy. The computing science will involve developing automated methods for identifying such objects from the LOFAR all-sky surveys data. The novel analysis tools developed in this project line will be made available to the community.

Key Words: Galaxy formation; Gravitational lensing; Black Holes

Fields of expertise involved: Radio Astronomy; Interferometry; Advanced calibration; Machine Learning

Supervisory team:

Institutes involved:
JBI
Kapteyn

Potential partners:

  • Netherlands Institute for Radio Astronomy (ASTRON)

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie SkÅ‚odowska-Curie grant agreement No. 754315.

Laatst gewijzigd:18 mei 2017 16:34