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The HClimRep project involves collaboration between leading climate researchers and AI experts from various institutions of the Helmholtz Association, including the Jülich Supercomputing Centre, the Alfred-Wegener-Institute in Bremerhaven, the Helmholtz center Hereon in Geesthacht, and the Karlsruhe Institute for Technology. This partnership ensures a strong interdisciplinary approach, combining expertise in atmospheric science, oceanography, AI, and high-performance computing.
Martin Schultz is the Group Leader of Earth System Data Exploration at the Institut Jülich Supercomputing Centre at Forschungszentrum Jülich and Division Co-lead for Large Scale Data Science. He holds a PhD in Physical Chemistry and a Habilitation in Meteorology, with a professorship in Computational Earth System Science at the University of Cologne since 2024. His research focuses on large-scale deep learning and FAIR data management for air quality, weather, and climate modeling. Previously, he held leadership roles at the Max-Planck-Institute for Meteorology and Forschungszentrum Jülich.
Thomas Jung is the Head of the Climate Dynamics Section at the Alfred Wegener Institute (AWI) and a Professor of Physics of the Climate System at the University of Bremen. He obtained his PhD in Meteorology from the University of Kiel in 2000. His research centers on the development and use of high-resolution kilometer-scale climate models, digital twins of the climate system, storyline approaches and the application of artificial intelligence and large language models to climate science. In addition to his current roles, he also serves as Vice Director of AWI and previously held the position of Senior Scientist at the European Centre for Medium-Range Weather Forecasts (ECMWF).
Peter Braesicke is Professor of Theoretical Atmospheric Physics at the Karlsruhe Institute of Technology (KIT) and Head of Research and Development / Member of the Executive Board at DWD. He holds a PhD in Meteorology from FU Berlin (1998) and has held various academic positions at KIT and the University of Cambridge. His research focuses on composition-climate interactions, atmospheric modeling including the stratosphere, and integrating AI/ML solutions in climate models. He has also contributed to the development of the ICON-ART model at KIT. Now at DWD he oversees the business area Research and Development.
David Greenberg is the Head of the Department of “Model-driven Machine Learning” at Helmholtz Centre Hereon and leads the Helmholtz AI Young Investigator Group in Earth and Environment. He earned his PhD from Eberhard Karl University of Tübingen in 2011. His research focuses on sub-grid-scale parameterizations, self-supervised learning, and deep learning-based simulation and forecasting. Previously, he held research positions at the Technical University of Munich and the Max Planck Institute for Biological Cybernetics. He is also a guest lecturer at the University of Hamburg.
Christian Lessig is a machine learning expert at ECMWF, the Eropean Center for Medium Weather Forecast. His background is in computer science but he also works today in scientific computing and numerical analysis. In the last years, his research moved towards addressing climate change, in particular by developing hybrid weather and climate simulation models that combine classical discretizations of the governing partial differential equations with neural networks that account for phenomena that are too expensive to simulate or whose physics is not well understood.
Ilaria Luise is a Senior Research Fellow at CERN, the European Center for Nuclear Research in Geneva. She works as a physicists within the Innovation Division of the CERN IT-Department. Her background is in high energy physics and big data management. She is Co-PI of the EMP2 project at CERN, which is part of the CERN Innovation Programme on Environmental Applications (CIPEA). The EMP2 project aims at implementing the AtmoRep model into a digital twin engine. This is performed in collaboration with the EU funded InterTwin project and the Digital Twin initiative at CERN.
Daniel Caviedes-Voullieme is the Team Leader of the Simulation and Data Lab for Terrestrial Systems at Jülich Supercomputing Centre and the Institute of Bio- and Geosciences Agrosphere, Forschungszentrum Jülich. He earned his PhD from Universidad de Zaragoza in 2013. His research focuses on ecohydrological and hydrodynamic modeling, Earth system modeling, and high-performance computing. Previously, he held academic positions at Brandenburg University of Technology Cottbus-Senftenberg, and postdoctoral roles at LIFTEC, University of Sheffield, and Universidad de Zaragoza. He has also worked as a Civil Engineer in Costa Rica.
Michael Langguth is a Postdoctoral Scientist at the Jülich Supercomputing Centre at Forschungszentrum Jülich, leading the ‘Deep Learning for Weather and Air Quality’ team within the ‘Earth System Data Exploration’ group. He earned his PhD and Master of Science in Meteorology from the University of Bonn. His research focuses on developing robust deep learning methods for meteorological applications, including statistical downscaling and super-resolution of air quality data. He is also involved in the AtmoRep project, applying AI technologies to atmospheric dynamics. Previously, he worked as a scientific researcher at Forschungszentrum Jülich and as a PhD student at the University of Bonn.
Belkis Asma Semcheddine is a Postdoctoral Researcher in Earth System Data Exploration at the Jülich Supercomputing Centre, Forschungszentrum Jülich. She completed her PhD in 2023 at the University of Boumerdes, Algeria, focusing on electrical and electronic engineering. Her recent research involves semantic segmentation of remote sensing imagery and applying large-scale deep learning to air quality, weather, and climate. She has previously taught mathematics and organic chemistry at the University of Boumerdes and machine learning at the FUTURIS Institute in Setif, Algeria.
Enxhi Kreshpa is a Scientific Programmer in Earth System Data Exploration at Jülich Supercomputing Center, Forschungszentrum Jülich. She holds an M.Sc. in Electronic Engineering from the Polytechnic University of Tirana. Her previous roles include Machine Learning Engineer and student trainee at Fujitsu Laboratories Ltd. Her current work focuses on data management and data-driven workflows, including co-administering the Jülich Meteocloud, managing AtmoRep data, and overseeing the DeepACF computing project.
Nikolay Koldunov is a Senior Scientist at the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, in Bremerhaven. He holds a Ph.D. in Physical Oceanography from the University of Hamburg and an M.Sc. in Applied Polar and Marine Science from the University of Bremen. His recent research focuses on the development of the FESOM2 unstructured ocean model, kilometer-scale ocean and climate modeling, and integrating Large Language Models for climate information. Previously, he has worked at MARUM, GERICS, and the Institute of Oceanography, University of Hamburg.
Julius Polz is a Doctoral Researcher at the Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research Atmospheric Trace Gases and Remote Sensing (IMK-ASF). He holds an M.Sc. in Mathematics from LMU Munich and has submitted his Ph.D. thesis at the University of Augsburg. His past research focused on applying AI methods to hydrometeorology, including neural network design for event detection, error correction, climate model downscaling, and post-processing seasonal forecasts. Within HClimRep he now focuses on representation learning of stratospheric circulation and variability.
Ana González-Nicolás Álvarez is a Research Scientist at the Jülich Supercomputing Centre, Forschungszentrum Jülich, where she is part of the Simulation and Data Lab for Terrestrial Systems. Her work focuses on the intersection of high-performance computing and hydrological and land surface modeling, contributing to exascale readiness. She earned her PhD in Civil Engineering from Colorado State University in 2014. Her research spans multiphase flow and transport in porous media, uncertainty quantification, Bayesian inference, and optimal design of experiments for environmental systems. Before joining Forschungszentrum Jülich, she held research positions at the University of Stuttgart, Lawrence Berkeley National Laboratory, and Colorado School of Mines, where she developed stochastic models for CO2 storage, leakage detection, and pressure management in deep saline reservoirs.
Nishant Kumar submitted his PhD dissertation at the Chair of Computer Graphics and Visualization, TU Dresden. His research focuses on Out-of-Distribution Detection, 2D Generative Modelling, and solving Inverse Problems using Invertible Neural Networks. Before his PhD, he earned an M.Sc. in Electrical Engineering from TU Dresden. He also brings industry experience, having worked as a Data Engineer at HCL Technologies and as a Software Engineer at Samsung India Electronics. During his PhD, he collaborated with Carl Zeiss Meditec AG on computer vision research. During his master’s studies, he worked as a Student Assistant at Fraunhofer IIS.
Ankit Patnala is a scientific co-worker at the institut Jülich Supercomputing Centre at Forschungszentrum Jülich, currently working with the ‘Deep Learning for Weather and Air Quality’ team within the ‘Earth System Data Exploration’ group. He holds an MSc in Mechanical Engineering from RWTH Aachen. Prior to this role, he focused on developing self-supervised learning techniques using remote sensing imagery, primarily analyzing crops. He has been involved in several projects, including explainable AI for ozone estimation, statistical downscaling, and AtmoRep.
Simon Grasse is a scientific programmer at the institut Jülich Supercomputing Centre at Forschungszentrum Jülich, currently focusing on supporting the Earth System Data Exploration group on software engineering tasks. After his training at Forschungszentrum Jülich, he is currently completing his Master in Applied Mathematics and Computer Science at FH-Aachen.