Project leader of the ACH Finland project. Fredric's research field is computational materials physics, with the specialization on high-dose damage in fusion materials and effects of irradiation of surfaces.
Vice Rector, PhD, Professor in Computational Materials Physics
Kai Nordlund is professor of computational materials physics and Vice rector of the University of Helsinki. In addition, he is leading together with three other senior scientist a 25-person research group doing quantum mechanical, classical and mesoscale atomistic simulations of radiation and other non-equilibrium effects in all classes of materials. As of 2022, he has published more than 560 refereed publications, and his h-index exceeds his age. Kai's contribution to ACH Finland is related to his fusion materials expertise.
Professor of Computer Science, Vice-head of ACH Finland
Jukka's research interests are in the engineering of large-scale data analytics and machine learning systems. Fusion research has a lot of exciting problems for applying these techniques and gaining new insights from experimental and simulated data.
Professor of Parallel and Distributed Data Science at University of Helsinki and Vice-Director of Helsinki Centre for Data Science (HiDATA), a world-class hub of Data Science in Helsinki. His research topics include Big Data, Parallel and Distributed Computing, Distributed Systems, and Verification Methods for Parallel Software. Keijo's contribution to ACH Finland is related to his data-structures and -management expertise, among others.
Senior Scientist at VTT, Visiting Scientist at UH
Aaro has more than a decade of experience in magnetic confinement fusion energy research. Current focus of ACH work on Bayesian methods for accelerating model validation.
Senior Application Scientist
Jan's specialization is in Fortran code parallelization and High Performance Computing.
Laurent is a specialist of turbulence and transport in plasma, and of high-performance computing in plasma physics. His current projects include ACH support of the EUROfusion TSVV11 task, in developing the surrogate model QLKNN-edge for QuaLiKiz, and participation to research in the TSVV4 task, in developing a full geometric Vlasov-Maxwell simulation and investigation of the limits of gyrokinetic simulations at the tokamak edge.
Oskar has a software engineering background and is doing high-performance research, working on improving the performance and scalability of scientific software.
Bruno is a computer scientist focusing on Machine Learning applied to Fusion research.
Adam works on improving machine learning methods for magnetic fusion devices, namely i) developing models for core-edge integration in tokamaks based on experimental and simulation data ii) improving the workflows of ML researchers in fusion by developing standardized data accessing/publishing methods.
Adam is part of the materials science department and works under a EUROfusion eneabling research grant for machine learning on power exhaust in tokamaks. However, he is closely involved in the ACH through collaboration with Aaro Järvinen on applications of Bayesian methods for uncertainty quantification and validation of predictive models in fusion plasmas, as well as with Jukka Nurminen and Fran Jurinec on the development of feature storage framework for improving machine learning workflows within EUROfusion.
The Sparselizard open-source C++ finite element (FE) library provides a framework for numerical implementation of multiphysics systems and domain-decomposition capabilities for high-performance computing. The collaboration aims to take advantage of these for numerical simulation of models describing the scrape-off layer (SOL) plasma.
Rahul's current work involves implementing FE algorithms to simulate SOL plasma in a self-consistent manner. Rahul's specialization is Multiphysics simulations surrounding Computational Fluid Dynamics. His background is Computational Mechanics.
Exploring how open-source technologies can be used to create a modern data platform for data-driven fusion research, with a focus on improving discoverability, re-usability, and provenance of data.
Emil works with uncertainity quantification and Bayesian methods for model validation.