Jonas Sukys

Dr. Jonas Sukys

Head of the Scientific Computing group

Department Systems Analysis, Integrated Assessment and Modelling

About Me

Head of the Scientific Computing group,
at the Department of Systems Analysis, Integrated Assessment and Modelling.

Research interests

[all publications: Google Scholar]

  • Massively parallel high performance computing (HPC), scalable parallelization, emerging computing platforms
  • Uncertainty Quantification and Propagation (UQ+P) for deterministic and stochastic models
  • Multi-level Monte Carlo (MLMC) methods for optimal hierarchical variance reduction in UQ+P
  • Hyperbolic nonlinear partial differential equations (PDEs): shallow water, multi-phase cavitation dynamics

Open positions

Currently there are no open positions available. Please contact me well in advance if you are interested in starting a new project in my group, and I will be glad to discuss a potential research proposal submission.

Research projects


Parallelization and deployment of scalable Particle Markov Chain Monte Carlo (PMCMC) framework implementing Bayesian uncertainty quantification for stochastic models in environmental data sciences. Multi-level variance reduction techniques and dynamic parallel load balancing are employed to mitigate significant computational requirements. Applications include realistic river invertebrate mesocosms using Individual Based Models (IBMs) and SDEs, ground water flow propagation using SPDEs, and stochastic 2-D urban flood analysis.

Collaboration: Peter Reichert, Nele Schuwirth, Gian Marco Palamara, Andreas Scheidegger (SIAM, Eawag) , Mira Kattwinkel (U Koblenz-Landau), Fabrizio Fenicia (SIAM, Eawag), Mario Schirmer (W+T, Eawag), Jörg Rieckermann, Joao Leitao (SWW, Eawag), Anze Zupanic, Piet Spaak (UTOX, ECO, Eawag), Torsten Hoefler (SPCL, ETH Zürich), Siddhartha Mishra (SAM, ETH Zürich), and the Swiss Supercomputing Center (CSCS).


Bayesian Inference for Geneva and other Lakes with remote sensing and in-situ measurement data assimilation using Kalman and particle filters, empirical dynamics methods and large scale numerical simulations of hydrological and ecological models. Multi-level variance reduction techniques are employed to allow for realistic hydrological model resolutions while keeping accurate uncertainty estimates.

Collaboration: Damien Bouffard (SURF, Eawag), Johny Wuest (EPF Lausanne), Siddhartha Mishra (SAM, ETH Zürich), Carlo Albert (SIAM, Eawag) , Andreas Scheidegger (SIAM, Eawag), Kris Villez (ENG, Eawag), Theo Baracchini (EPFL), Fabio Nobile (EPFL), Hakon Hoel (Chalmers), Alexey Chernov (Oldenburg), Fernando Perez Cruz (SDSC) and the Swiss Data Science Center (SDSC).


Parallelization potential study for the Hamiltonian Monte Carlo methods with potential applications in water catchment modeling and solar physics.

Collaboration: Carlo Albert (SIAM, Eawag), Simone Ulzega (ZHAW), Antonietta Mira (USI Lugano), Christian Rueegg (PSI) and the Swiss Data Science Center (SDSC).


Coupling of the PyMLMC parallel sampling framework and the non-hydrostatic multi-layer solver for landslide induced tsunami wave propagation. Movie: link.

Collaboration: Manuel Castro (CADMOS, U Malaga, Spain) and Siddhartha Mishra (SAM, ETH Zürich).


Uncertainty quantification using optimal fidelity multi-level Monte Carlo for large scale direct numerical simulations of cloud cavitation collapse. Partly supported by CSCS, PRACE and INCITE allocations of supercomputer access to PIZ DAINT, JUQUEEN, FERMI and MIRA. Movies: SC15, SC16.

Collaboration: Ursula Rasthofer, Fabian Wermelinger, Panagiotis Hadjidoukas and Petros Koumoutsakos (CSElab, ETH Zürich).

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Scalable Bayesian inference framework for uncertainty quantification in stochastic models using thousands of processors in parallel at the Swiss Supercomputing Center and ETH Zurich.
Heterogeneous data platform for operational modeling and forecasting of Swiss lakes in collaboration with the Swiss Data Science Center.

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Phone: +41 58 765 5310
Fax: +41 58 765 5802
Address: Eawag
Überlandstrasse 133
8600 Dübendorf
Office: FC D10

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