Dr. Moritz Ziegler is a geomechanics researcher affiliated with the Technical University of Munich (TUM) and the Assistant Professorship of Geothermal Technologies . He previously worked at GFZ Potsdam (2017–2023) and earned his PhD in Geophysics from the University of Potsdam (2014–2017). His research focuses on geomechanical numerical modeling , uncertainty quantification , and 3D stress field analysis , with applications to geothermal energy, seismic hazard, and rock mechanics. Education: Bachelor of Science in Geosciences (2008–2011), Freie Universität Berlin Master of Science in Geophysics (2011–2014), University of Potsdam PhD in Geophysics (2014–2017), University of Potsdam & GFZ Potsdam His work integrates advanced computational methods ( Altair Hypermesh , Dassault Systèmes Abaqus , Python , Matlab ) to address complex geomechanical problems. Recent publications analyze stress gradients in the North Alpine Foreland Basin, fault-stress interactions, and physics-based machine learning in geomechanics. He has contributed to software development (DOuGLAS v1.0) and calibration tools (FAST Calibration v2.4). Scientific awards or honors are not explicitly mentioned in the provided text. However, his research has been published in leading journals such as Geophysical Journal International , Solid Earth , and Pure and Applied Geophysics .
Dr Ting Sun is an Associate Professor in Climate & Meteorological Hazard Risks at University College London , Department of Risk and Disaster Reduction. He earned his BEng (2009) and PhD in Hydrology (2013) from Tsinghua University , followed by a visiting period at Princeton University (2011–2012). After postdoctoral appointments at Tsinghua and the University of Reading , he held a NERC Independent Research Fellowship at Reading (2017–2022) before joining UCL in May 2022. Education PhD in Hydrology, Tsinghua University, 2013 BEng in Hydraulic Engineering, Tsinghua University, 2009 Visiting PhD Student, Princeton University, 2011–2012 Research Interests Dr Sun’s work converges on urban climate modelling across scales —from neighbourhood blocks to global grids—focusing on the impacts of weather and climate extremes such as heat waves and extreme rainfall in cities. He is the lead developer of the Surface Urban Energy and Water balance Scheme (SUEWS) and its Python wrapper SuPy , developed in collaboration with Prof Sue Grimmond’s micromet group. He also contributes as a core member of the Urban Multi-scale Environmental Predictor (UMEP) development team. His multidisciplinary expertise integrates hydro-climate dynamics, computational modelling, machine learning, built-environment processes, and public-health linkages . Research Trends from Recent Publications Across the 15 most recent articles, a clear trajectory emerges from high-resolution urban-process modelling toward integrated socio-environmental assessments . Studies published in 2024–2025 couple atmospheric models (WRF-SUEWS) with global building-morphology datasets (GLAMOUR) to quantify how cities alter rainfall patterns, temperature sensitivity, and heat-related mortality. Earlier works progressively refined SUEWS’s physical parameterisations and Python accessibility, while recent outputs leverage deep-learning remote-sensing tools (SHAFTS) and hybrid hydrological-neural architectures to deliver actionable insights for urban planning and climate adaptation. Scientific Awards & Fellowships NERC Independent Research Fellowship , University of Reading, 2017–2022 HEA Fellowship , University College London, 2023 Professional Service & Editorial Roles Topic Editor , Geoscientific Model Development (from 2025) Editorial Board Member , Scientific Data (from 2024) Peer review and consultancy for journals, conferences, and policy bodies Supervision of taught-course projects and research degrees External examining and mentoring Labs, Teams & Collaborations Dr Sun leads and collaborates within the UCL Department of Risk and Disaster Reduction , working closely with the micromet group at the University of Reading (Prof Sue Grimmond) on SUEWS/SuPy development. He is an active member of the UMEP consortium and maintains extensive international collaborations spanning Tsinghua University, Princeton, and numerous European research centres, underpinning a vibrant, interdisciplinary research network focused on urban climate resilience.
Dr. Martin Lange is part of the Project Group Ecological Epidemiology within the Department of Ecological Modelling at the Helmholtz Centre for Environmental Research (UFZ). His research focuses on computational epidemiology, particularly in wildlife and livestock systems. Key areas include disease transmission dynamics, surveillance strategies, and the development of predictive models for infectious diseases like African Swine Fever (ASF) and Bovine Viral Diarrhea Virus (BVDV). Affiliations: UFZ, Department of Ecological Modelling; EcoEpi research group. Education: PhD in Veterinary Epidemiology (2013, TU Bergakademie Freiberg). Research interests span ecological epidemiology, wildlife disease control, and the application of agent-based and individual-based models to understand pathogen spread. Notable contributions include modeling ASF in wild boar populations and BVDV eradication strategies in Ireland. Awards: Received the 2018 UFZ-Wissenstransferpreis (shared with H.H. Thulke) and the Konrad-Bögel-Nachwuchsförderpreis for his doctoral work on eco-epidemiology of wild boar diseases. Also recognized at the DACh Epidemiologietagung 2011. Publications: Over 50 peer-reviewed articles since 2006, emphasizing interdisciplinary approaches to disease modeling and environmental systems. Recent work includes digital twin frameworks for biodiversity monitoring and Python-based model coupling tools like FINAM. Labs/Teams: Active in the EcoEpi group and contributes to projects like BioDT (Biodiversity Digital Twin) and CauSES (Causation in Social-Ecological Systems).
Prof. Dr. Jochen Garcke is a faculty member at the Institute for Numerical Simulation, University of Bonn, with a dual affiliation at Fraunhofer SCAI's Department of Numerical Data-Based Prediction. His work bridges numerical simulation and machine learning, focusing on high-dimensional problems, sparse grids, and optimal control. Key research themes: Sparse grids, machine learning for simulations, reinforcement learning, uncertainty quantification Teaching includes courses on Numerical Methods in Science and Technology and Scientific Computing , emphasizing practical machine learning applications. Recent publications explore hybrid models combining data-driven and physics-based approaches in automotive engineering, wind turbines, and geoscientific modeling. His group employs adaptive sparse grids, graph algorithms, and spectral methods to tackle challenges in crash simulations, fluctuating renewable energy systems, and turbulent flow analysis. Collaborations span Fraunhofer SCAI and industry 4.0 initiatives.
Lina von Sydow is a Professor in Computational Science at Uppsala University's Department of Information Technology. She serves as Section Dean for the Mathematical-Computer Science Section since July 2023. Her academic journey includes becoming an Associate Professor in 2000, Senior Lecturer since 1997, and leading the Department of Information Technology from 2018 to 2023. PhD in Domain Decomposition Methods (1995, Uppsala University) Postdoctoral Fellow at Oxford University (1996-1997) Her research spans computational science with dual focuses on Computational Finance and Ice Sheet Modeling . In finance, she develops numerical methods for option pricing using PDEs, radial basis functions, and stochastic volatility models. In climate science, she contributes to ice sheet dynamics through full Stokes models and adaptive time-stepping approaches, particularly in simulating grounding line migration. Recent publications (2025) address gender disparities in IT education, including comparative analysis of admission trends and intervention studies to boost female enrollment. Earlier works (2020-2015) focus on high-order finite difference methods for financial derivatives, BENCHOP benchmarking projects, and preconditioning techniques for PDEs. Scientific awards include Excellent Teacher (2013) She actively collaborates on educational reforms, co-authoring studies like Gender-aware course reform in Scientific Computing (2013). Her leadership roles include Head of Department (2018-2023) and Section Dean (2023-present), influencing academic governance and interdisciplinary research. Labs and teams: Works with Uppsala University's Computational Science group, Elmer/ICE project collaborators (e.g., Per Lötstedt, Gong Cheng), and international partners in numerical finance and climate modeling.
Boris Kaus is a Full Professor and Chair of Geophysics and Geodynamics at the Institute of Geosciences, Johannes Gutenberg University Mainz, Germany. His research focuses on understanding geological processes from grain scale to planetary scale using mathematical and numerical models. Funded by the German Research Foundation, European Research Council, and BMBF, his work spans lithospheric deformation, melt migration, fold-and-thrust belts, and high-performance computing applications in geosciences. His research interests center on geodynamic modeling of lithospheric processes, including subduction zones, mantle convection, and crustal deformation. Kaus develops novel numerical approaches to simulate complex geological phenomena, with emphasis on coupling between erosion, lithosphere dynamics, and mantle flow. His group creates specialized software for high-performance computing systems to tackle multi-scale geophysical problems. His scientific awards include the Paul Niggli Medal, EGU Arne Richter Award, multiple ERC grants (Starting, Proof-of-Concept, Consolidator), and the Carl Friedrich Gauss Lecturer honor. He has received recognition for editorial contributions including G-Cubed's Excellence in Refereeing award. ERC Consolidator Grant MAGMA (2018-2023) ERC Proof of Concept Grant SALTED (2016-2017) ERC Starting Grant MODEL (2010-2015) John von Neumann Excellence Project for HPC ETH Medal for Ph.D. thesis Kaus actively supervises graduate students and leads research projects funded by major European and German agencies. His group develops open-source software like GeophysicalModelGenerator.jl and maintains strong collaborations with international institutions including ETH Zürich and USC. Current projects focus on magma dynamics, lithospheric shear localization, and the development of advanced numerical methods for geodynamic simulations. The research group operates within the Geodynamics & Geophysics team at JGU Mainz, utilizing high-performance computing resources and collaborating with multiple European research initiatives including IMPRS and FORTHEM networks. Their laboratory specializes in numerical modeling of Earth systems with applications to tectonics, volcanology, and crustal evolution.
Juan Antonio Añel Cabanelas is a Professor of Earth Physics at the University of Vigo , affiliated with the EPhysLab research group and the Specialized Group on Atmospheric and Ocean Physics of the Royal Spanish Society of Physics . He serves as an Executive Editor for Geoscientific Model Development and an Associate Editor for PLoS Climate . PhD in Physics (2007) from the University of Vigo, thesis: Climatic analysis of the tropopause using radiosonde data Taught courses in Meteorology, Atmospheric Physics, Computational Science, and Renewable Energy at the University of Vigo and international institutions His research focuses on climate change impacts , upper troposphere-lower stratosphere dynamics , renewable energy modeling , and computational reproducibility in climate research . He emphasizes instrumental data recovery and open science , with recent work addressing stratospheric contraction and mercury cycling . Key publications span extreme weather-energy sector interactions , Fortran code quality , and ozone data analysis . He mentors PhD students in Physics and Computer Science, and has collaborated with institutions in Mexico, Portugal, and the private sector. He advocates for free software and has organized workshops on climate intervention and citizen science . His work is funded by public grants from Spain's Government, Xunta de Galicia, and private entities like Naturgy and Acciona, with computing support from Google and Microsoft.
Dr. Christoph Müller is a leading scientist at the Potsdam Institute for Climate Impact Research (PIK), Germany, where he has served as working-group leader of the Land Biosphere Dynamics group since 2012. He also co-leads the Global Biosphere and Water Modeling team and acts as the scientist-in-charge for the internationally renowned LPJmL global vegetation and crop model. Additionally, he is Co-lead of the Ag-GRID initiative within the Agricultural Model Intercomparison and Improvement Project (AgMIP) and serves as Topical Editor for Geoscientific Model Development . Education Diploma in Geoecology, University of Potsdam (2002) PhD in Geoecology, University of Potsdam & International Max Planck Research School (IMPRS) (2007) Research Interests Dr. Müller’s research centres on understanding and modelling the interactions between climate, land use, and the biosphere to support sustainable food-system transformations. His work integrates global-scale vegetation and crop models with climate projections, socio-economic scenarios, and observational data to assess: Impacts of climate change and extreme events on crop yields and food security Carbon, nitrogen, and water cycles in managed and natural ecosystems Land-based climate-mitigation strategies and their co-benefits or trade-offs Adaptation options for agriculture under global change He promotes open science and reproducible modelling workflows, exemplified by the LPJmL open-source ecosystem model and associated toolkits. Publication Profile & Trends Since 2014 he has authored or co-authored more than 200 peer-reviewed articles. Recent work (2024-2025) highlights three dominant themes: (1) quantifying underestimated negative impacts of climate extremes on crop yields, (2) assessing the sustainability of large-scale land-based mitigation measures, and (3) advancing model intercomparison frameworks (e.g., AgMIP, ISIMIP) to reduce uncertainty in global yield projections. His studies increasingly integrate economic and health perspectives, examining how dietary shifts and food-system transformations can achieve climate, environmental, and social co-benefits. Scientific Awards & Recognition While no formal awards are explicitly listed, several publications have received notable recognition: “Soil quality both increases crop production and improves resilience to climate change” (Nature Climate Change, 2022) – listed among China’s top ten major advances in agricultural science in 2023. “Large potential for crop production adaptation depends on available future varieties” (Global Change Biology, 2021) – top-downloaded article. “Climate change impacts on global agriculture emerge earlier in new generation of climate and crop models” (Nature Food, 2021) – widely cited in IPCC AR6. Leadership, Grants & Collaboration Dr. Müller leads or co-leads multiple international projects and working groups: Working Group Leader – Land Biosphere Dynamics, PIK Research Department 2 Co-Lead – Ag-GRID, Agricultural Model Intercomparison and Improvement Project (AgMIP) Scientist-in-Charge – LPJmL model development and application Topical Editor – Geoscientific Model Development journal These roles involve coordinating multi-institutional consortia, securing competitive grants, and mentoring early-career researchers. Laboratory & Data Resources Dr. Müller’s “team” is essentially the LPJmL modelling group at PIK, comprising post-docs, doctoral researchers, and software engineers who maintain and extend the LPJmL code-base, develop satellite-data fusion products, and provide model-driven policy support to governments and international organisations such as the IPCC, FAO, and World Bank.
Mario Costa Sousa is a Professor in the Department of Computer Science at the University of Calgary. He holds a BSc from Catholic University of Petrópolis (1989), an MSc from Pontificia Universidade Católica do Rio de Janeiro (1994), and a PhD in Computer Science from the University of Alberta (1999). His primary research focuses on computer graphics, scientific visualization, and sketch-based modeling with applications to geoscience and reservoir engineering. He leads the illustrares research group, which develops novel visual computing paradigms for interdisciplinary challenges in science, engineering, and design. Research interests include non-photorealistic rendering, sketch-based interfaces, visualization/visual analytics, and human-data interaction. His work integrates geoscientific data with interactive modeling tools to address reservoir uncertainty and enhance decision-making in energy and environmental domains. Recent projects involve rapid prototyping of subsurface CO2 sequestration models and machine learning for drill core analysis. He teaches courses such as CPSC 591/691 Rendering (Fall 2024), CPSC 319 Data Structures (Winter 2025), and DATA 501 Data Science Capstone (Winter 2025). His research has contributed to energy innovation initiatives at the University of Calgary, focusing on geothermal energy and unconventional reservoirs. Key collaborations include work on Gaussian process-based uncertainty quantification, functional data analysis for production forecasting, and immersive VR tools for surgical training (e.g., NeuroSimVR and JackVR oil rig simulators). His publications span reservoir modeling, geological visualization, and interactive systems for spatial data exploration.
Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.
apl. Prof. Wolfgang Gossel is a Senior Scientist and Adjunct Professor in Hydrogeology at Martin Luther University of Halle-Wittenberg. His research focuses on groundwater vulnerability assessment, hydrogeological modeling system coupling, and large-scale GIS applications in hydrology. He leads a multidisciplinary team including Dr. Christoph Bott, Dr. Andreas Englert, and others. Education: 1990: Diploma in Geology, RWTH Aachen University 1999: Doctor of Natural Sciences, Freie Universität Berlin 2008: Habilitation (venia legendi in Applied Geology), MLU Research Interests: Specializes in applied geology challenges such as groundwater system modeling interfaces and vulnerability analysis. His work bridges theoretical hydrogeology with practical GIS applications for environmental management. Publications: Recent work includes studies on groundwater vulnerability in India, paleo-saltwater intrusion modeling in Northeast Africa, and large-scale aquifer system analysis. Themes emphasize interdisciplinary approaches to complex hydrological systems. Team & Infrastructure: Manages a lab with researchers and assistants focusing on geospatial hydrology. Coordinates international collaborations through projects like the Nubian Aquifer System modeling.
Dr. Irene Manzella is an Associate Professor in the Department of Applied Earth Sciences at the University of Twente. Her research focuses on landslide dynamics, volcanic processes, and granular flow mechanics with applications to natural hazard mitigation. She specializes in experimental geophysics, combining field observations, laboratory simulations, and numerical modeling to understand mass wasting phenomena. Key research areas include debris avalanche propagation mechanisms, sedimentological analysis of volcanic deposits, and the role of particle concentration in gravitational instabilities within volcanic clouds. Manzella's work integrates smart sensor technologies for real-time landslide monitoring and develops innovative methods for disaster impact assessment, such as multi-hazard dashboards and EO-based risk frameworks. Her collaborative projects involve creating flood and earthquake hazard maps for Dominica, developing immersive visualizations for scientific communication, and organizing conferences like the NEEDS conference 2023. Manzella contributes to open-source tools like Python workflows for satellite data processing and has published over 35 peer-reviewed articles in journals like Geomorphology and Frontiers in Earth Science . Recent work emphasizes bidisperse granular flows' scale-dependent behavior, smart sensor applications for tracking debris movement, and improving volcanic hazard predictions through experimental validation of ash cloud dynamics. Her research bridges engineering, geology, and computer science to enhance disaster resilience strategies globally.
Dr. Lucia McCallum is a Senior Lecturer in Geodetic VLBI at the School of Natural Sciences, Physics, University of Tasmania. She specializes in Very Long Baseline Interferometry (VLBI) for Earth observation, focusing on measuring the Earth's shape, rotation, polar motion, and plate tectonics. Her work connects geodetic techniques (GNSS, SLR, VLBI) via space tie satellites, with leadership in the ARC DECRA-funded project and the AuScope VLBI collaboration with Geoscience Australia. PhD from Technische Universität Vienna Active since 2014 in UTAS radio astronomy group Her research bridges geodesy and astrophysics, advancing the next-generation VLBI Global Observing System (VGOS) for millimeter-level precision. This technology addresses critical geoscientific challenges like sea level rise prediction and terrestrial reference frame stability. Recent publications (2025-2014) span VGOS optimization, GNSS satellite tracking, atmospheric signal corrections, and space tie methodologies. Her work emphasizes international collaboration through the International VLBI Service for Geodesy and Astrometry (IVS) and leadership roles in the Asian Oceania VLBI Group (AOV) and IVS Working Group 7. Scientific Awards Australian Research Council DECRA Fellowship Austrian Science Fund (FWF) Schrödinger Fellowship She supervises doctoral and master's students in geodetic VLBI projects, including topics like space weather effects, dynamic scheduling, and satellite signal calibration. Her full description includes global network operations, technical innovations, and societal impact through precision geodetic infrastructure.
Margarete A. Jadamec serves as Associate Professor at the University at Buffalo, SUNY, holding dual appointments in the Department of Earth Sciences within the College of Arts and Sciences and the Institute for Artificial Intelligence and Data Science. She directs the Computational and Data-Enabled Science and Engineering PhD Program, leading interdisciplinary research at the intersection of geodynamics and advanced computing. Her research focuses on solid Earth deformation processes, particularly plate and mantle dynamics at subduction zones. Using high-resolution numerical models and high-performance computing, she investigates how 3D slab geometries influence mantle rheology and subduction mechanics. Key interests include slab-driven mantle flow, multi-plate interactions, and dynamically evolving plate-asthenosphere decoupling, moving beyond traditional 2D paradigms toward comprehensive 3D modeling. Her publication record (2014-2018) reveals a strong emphasis on computational geodynamics, with recurring themes in mantle flow dynamics, subduction zone mechanics, and virtual reality applications for geoscientific visualization. Notable contributions include developing the ShowEarthModel framework for global slab geometry analysis and advancing 3D modeling of the Alaska tectonic system. Dr. Jadamec actively mentors students through the Department of Earth Sciences and Computational and Data-Enabled Science and Engineering PhD Program, collaborating with UB's Research Engineering Laboratory for Advanced Computational Science. Her Geodynamics Research and Visualization Group leverages modern technologies to pioneer convergent research in plate tectonics. Laboratory work centers on the Geodynamics Research and Visualization Lab, where her team develops data-driven models of natural subduction systems using high-performance computing and 3D virtual reality frameworks like TECT_Mod3D Software and geosolver Cluster Partition.
Bill Collins is a Professor at the University of Reading, specializing in climate science, atmospheric chemistry, and climate modeling. His research focuses on aerosols, greenhouse gases, and their interactions with climate systems. He has contributed to major international initiatives including the IPCC's Sixth Assessment Report and the AerChemMIP project. Key areas of expertise include climate metrics, methane dynamics, and the impacts of short-lived climate forcers. Collaborations span institutions globally, with a focus on model intercomparison projects and policy-relevant climate science. His work bridges climate science and environmental policy, addressing topics like hydrogen economy climate benefits, pandemic lockdown emissions, and carbon budget constraints. He has published extensively in journals such as Geoscientific Model Development , Atmospheric Chemistry and Physics , and Nature Climate Change . Contributions to climate models like HadGEM2 and UKESM1 highlight his role in advancing Earth system modeling frameworks. His research emphasizes urgent climate mitigation pathways and the societal implications of atmospheric changes.
