Vacancies

Tenure Track position in Mathematics and Computer Science for Life Sciences and Health

The CWI Life Sciences and Health (LSH) group is a group of computer scientists and mathematicians who have their research focus on the analysis and design of models and algorithms and their direct application to important challenges in the LSH domain. Our present research team of computer scientists and mathematicians has expertise in e.g. computational genomics, medical informatics, computational phylogenetics and biological network analysis. We come from diverse methodological backgrounds, such as computational intelligence, computational data science and operations research. Methodologically, we develop new theories, models, algorithms and decision support tools, mostly for problems arising in collaboration with experimental biologists and medical experts. We also actively collaborate in projects with academic hospitals, biological and biochemical research institutes and industry.

Innovation Centre Manager EIT Digital Amsterdam (0,5 fte)

Het Innovation Centre is in november 2017 opgericht 1) om universiteiten, onderzoeksinstituten, bedrijven en overheidsorganisaties in Amsterdam te verbinden met het Europese EIT Digital netwerk, 2) om het netwerk van scale-up bedrijven uit te breiden, en 3) om een Industrial Doctorates Programme te beginnen op het thema Digital Finance. De eerstkomende jaren zijn de innovatieactiviteiten voornamelijk gericht op de thema’s Digital Finance en Digital Cities. Het Innovation Centre is gehuisvest bij het NWO-onderzoeksinstituut Centrum Wiskunde & Informatica (CWI) op het Amsterdam Science Park. CWI ondersteunt het Innovation Centre door de manager aan te stellen en door kantoorruimte, ruimte voor bijeenkomsten, ICT-voorzieningen en secretariële ondersteuning beschikbaar te stellen.

PhD Student on the subject of Ultra-Fast 3D Reconstruction

Centrum Wiskunde & Informatica (CWI) and the Mathematical Institute of Leiden University seek a dedicated candidate for a PhD position in the interdisciplinary field of Mathematical Analysis, Algorithms and Computation for Tomography. As a PhD student, you will be part-time embedded in the Computational Imaging group at CWI, and part-time in the Analysis and Dynamical Systems group of the Mathematical Institute of Leiden University. As a team, we develop cutting-edge techniques for advanced tomographic reconstruction, combining expertise from Mathematics (Inverse Problems and Dynamical Systems), Computer Science (Efficient Algorithms and High Performance Computing), and Physics (Image Formation Modelling). Our goal is to develop novel computational methods for 3D and 4D imaging. We work closely with research colleagues both inside the hosting institutes as well as externally, with industrial partners and with international X-ray and electron imaging groups. Your PhD project will be part of the Marie Skłodowska-Curie Innovative Training Network, “Multiscale, Multimodal, Multidimensional imaging for EngineeRING” or in short MUMMERING. The network is concerned with interdisciplinary R&D and training in 3D imaging and its application in materials- and information science. The network offers totally 15 PhD positions. You can find information about all 15 PhD positions at https://euraxess.ec.europa.eu/jobs/256053 .

Postdoc on the subject of Machine Learning and Space Weather

The position involves research in machine learning techniques and Bayesian inference, applied to real-time forecasting of energetic electrons in the Earth radiation belts. These electrons can be harmful to satellites, whose disruption can potentially lead to catastrophic societal and economic events. The emerging field of Space Weather is concerned with making accurate predictions of such dangerous events, sufficiently in advance so that countermeasures can be taken. The aim of this project is to advance our space weather prediction capability by enhancing physics based models with a new data-driven probabilistic framework. The project will involve state-of-the-art numerical simulations, Bayesian parameters estimation, uncertainty quantification and machine learning techniques.

Postdoc in the research project "Approximation Algorithms, Quantum Information and Semidefinite Optimization"

The research project “Approximation algorithms, quantum information and semidefinite optimization” aims to explore the limits of efficient computation within classical and quantum computing, using semidefinite optimization as a main unifying tool. The position involves research into the mathematical and computer science aspects of approximation algorithms for discrete optimization, quantum entanglement in communication, and complexity of fundamental problems in classical and quantum computing. More information about the project can be found at this website.

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