جست‌وجوی استاد و پژوهشگر

Nicole Reinfeldمشاهده پروفایل

پژوهشگر

• Transportation Planning
• Commuting Behavior Analysis
• Cluster Analysis
• +۳ مورد دیگر

Nicole Reinfeld is a Researcher at Frankfurt University of Applied Sciences, affiliated with Department 1: Architecture, Civil Engineering, and Geomatics. Her work focuses on transportation planning, cycling mobility, and data-driven analysis of urban commuting behavior. Specializes in entropy balancing and cluster analysis for decomposing mobility trends Key projects: ModelRad (bicycle traffic modeling), ParkRight (parking violation analysis) Active in teaching econometrics and production logistics courses Her research spans methodological innovations like zero-inflated regression models and behavioral studies on weather-clothing interactions. She contributes to policy discussions through conference presentations and publications in journals such as Transportation Research Record. Collaborates with colleagues like Dr. Tobias Hagen and Dr. Shahram Saki, particularly on parking violation studies. Leads focus group methodologies and participates in mobility tech development (e.g., FlowMo gamification app).

Stefan Niessenمشاهده پروفایل

استاد

• Renewable Energy Systems
• Energy Efficiency
• Smart Grids
• +۳ مورد دیگر

Prof. Dr. Stefan Niessen is a Professor at Technische Universität Darmstadt, leading the Department of Mechanical and Process Engineering. His research focuses on energy systems optimization, decarbonization strategies, and smart grid technologies. He specializes in thermal energy management, renewable energy communities, and multi-modal energy markets. His work addresses challenges such as grid stability, distributed energy resource integration, and efficient district heating operations. Key research interests include: Optimization of energy networks (e.g., district heating, MV/LV grids) Data-driven approaches for energy system control Flexibility management in hybrid thermal-electric systems Impact of tariff designs on energy market dynamics Recent studies highlight advancements in: Thermo-hydraulic oscillation mitigation in heating systems Price-collecting Steiner trees for topology design Low-voltage grid control via reinforcement learning His work emphasizes practical implementation, with contributions to German energy policy, industrial decarbonization pathways, and resilient energy infrastructure design.

Matthias Karlbauerمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Physics-Aware Neural Networks
• +۱۲ مورد دیگر

Matthias Karlbauer is a Postdoctoral Researcher in the Cognitive Modeling group at the Wilhelm Schickard Institute for Computer Science, University of Tübingen. He is currently working in the Land-Atmosphere Feedback Initiative (LAFI), focusing on physics-aware machine learning for climate and environmental modeling. His work bridges cognitive science, artificial intelligence, and geophysical systems. PhD in Cognitive Modeling, University of Tübingen (2019–2024) Master of Cognitive Science, University of Tübingen (2015–2018) Bachelor of Cognitive Science, University of Tübingen (2012–2015) Scholar, International Max Planck Research School for Intelligent Systems (IMPRS-IS) His research centers on physics-aware neural networks , spatiotemporal data prediction , and deep learning for environmental systems . He develops models like DISTANA and finite volume neural networks to integrate physical laws into neural architectures, enabling robust forecasting of temperature, geopotential, and fluid dynamics. His interests also extend to generative models, recurrent networks, and graph neural networks applied to climate and sustainability challenges. The recent publications show a strong trend toward integrating partial differential equations with neural networks, denoising spatiotemporal signals , and modeling physical processes using hybrid AI. His work emphasizes interpretability, physical consistency, and real-world applicability in climate science and cognitive modeling. Matthias has actively supervised multiple Bachelor’s and Master’s students on projects related to neural network applications in physics and climate data. He has contributed to teaching as a tutor and lecturer in courses such as Generative and Recurrent Neural Networks , Advanced Artificial Neural Networks , and Graph Neural Networks . While no formal grants are mentioned, his IMPRS-IS affiliation suggests institutional funding support. He is part of the Cognitive Modeling research group at the University of Tübingen, collaborating on projects involving neural modeling of cognitive and physical processes, with a focus on sustainability and climate protection.

• Geospatial Digital Twins
• Virtual Geographic Environments
• Remote Sensing
• +۷ مورد دیگر

Jun Zhu is a professor at the Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China. His research is centered on geospatial digital twins, virtual geographic environments, and intelligent visualization for disaster risk management, with strong interdisciplinary work in AI, remote sensing, and VR-based simulation. His research interests include: Geospatial Digital Twins Virtual Geographic Environments AI for Remote Sensing 3D and VR-based Disaster Visualization Knowledge Graphs in GIS Public Risk Communication The recent articles (2023–2025) demonstrate a strong trend in integrating large language models, knowledge graphs, and deep learning with geospatial data to build intelligent, interactive, and immersive systems for infrastructure monitoring, disaster simulation, and public engagement. His work emphasizes data-knowledge fusion, human-centered visualization, and real-world applicability in urban and environmental contexts. Scientific Awards: No awards listed in the provided text. Advising and Grants: While Jun Zhu has extensive collaboration with researchers such as Weilian Li, Qing Zhu, Yakun Xie, and Jianbo Lai, and appears to lead research projects, there is no explicit mention of student advising, grant funding, or project titles in the provided data. Labs and Teams: Jun Zhu is likely part of a research group focused on digital twins and geospatial AI at Southwest Jiaotong University, collaborating closely with colleagues in geoinformatics and remote sensing, though specific lab names are not mentioned.

• Public Economics
• Labor Supply
• Inequality and Redistribution
• +۵ مورد دیگر

Max Löffler is an Assistant Professor in the Department of Microeconomics and Public Economics at Maastricht University and a Research Fellow at the same institution since December 2023. He is also affiliated with the ERC-funded HIPPO project at the University of Cologne, reflecting his strong ties to leading European economic research centers. His research focuses on public economics and labor economics, with particular emphasis on taxation, inequality, labor supply modeling, and urban/regional economic issues. His work combines structural econometric methods with empirical analysis to evaluate the welfare and distributional effects of public policies. The recent publications reveal a consistent trend in studying the real-world impacts of taxation, labor market policies, and inequality, while expanding into newer domains such as climate change exposure and the long-term economic consequences of surveillance. His research often involves collaboration with prominent economists and utilizes large-scale microdata and policy simulation models. IZA Discussion Papers and Standpunkte Published in journals: American Economic Journal: Economic Policy , Journal of the European Economic Association , Journal of Economic Inequality , Labour Economics , German Economic Review , Perspektiven der Wirtschaftspolitik Löffler has advised or contributed to policy-relevant research through institutions such as IZA, ifo Institute, and ZEW. He has not received any explicitly mentioned scientific awards in the provided text. He does not appear to lead a named lab or research team, but is deeply embedded in collaborative research networks focused on public policy evaluation.

Anna Krauseمشاهده پروفایل

پژوهشگر

• Machine Learning
• Deep Learning
• Time Series Analysis
• +۷ مورد دیگر

Dr.-Ing. Anna Krause is a researcher at the Chair of Data Science (Informatik X) within the Faculty of Mathematics and Computer Science at the University of Würzburg. She leads the Deep Learning for Dynamical Systems Group and has been actively involved in teaching at the university since 2019, including courses on Machine Learning for Time Series Analysis and Data Mining. Doctoral degree in Electrical Engineering (2019), University of Hannover Diploma in Electrical Engineering (2009), Technical University Dresden Her research focuses on Environmental Sensing and Time Series Analysis , particularly on enhancing physics-based models using machine learning techniques for meteorological applications and sparse sensor networks. She has made significant contributions to explainable AI, climate modeling, and fraud detection systems. Anna's recent publications demonstrate expertise in climate modeling (ConvMOS, ICLR 2024-2025), physics-informed neural networks (TaylorPDENet, ECMLPKDD 2023), and fraud detection (MIDAS workshops, ECMLPKDD 2020-2023). She actively contributes to conferences as organizer and PC member, including ECMLPKDD and ICLR workshops. Scientific Awards Best ML Innovation Award (2020) for Deep Learning in Climate Modeling Best Student Paper Award (2020) for Multi-Task Land Use Regression Best Paper Award (2020) for Financial Fraud Detection with INALU The DynaBench dataset introduced in 2023 provides benchmark tools for learning dynamical systems from low-resolution data. Her work combines theoretical advancements with practical implementations, including edge computing applications for beekeeping monitoring systems.

• Plate Tectonics
• Geology
• Geophysics
• +۴ مورد دیگر

Maryline Moulin is a prominent geophysics researcher specializing in plate tectonics, continental margin evolution, and integrated seismic analysis. She has led major oceanographic campaigns including PAMELA-MOZ05/03 and MAGIC projects, focusing on passive margins in the South Atlantic and East African regions. 2003 PhD from University of Western Brittany 2016 Project Leader for PAMELA campaigns Key collaborator in 15+ international seismic studies Her research spans diverse methodologies: Wide-angle seismic (OBS) profiling Multi-channel seismic (MCS) interpretation Gravity and magnetic anomaly analysis 3D structural modeling (Placa3D) Seismo-stratigraphic analysis Recent publications (2023-2025) reveal patterns in: Proto-oceanic crust formation Passive margin segmentation Quaternary contourite systems Transform margin tectonics Deep crustal architecture

Vitus Bensonمشاهده پروفایل

پژوهشگر

• Deep Learning
• Biogeochemistry
• Climate Risk Mitigation
• +۴ مورد دیگر

Vitus Benson is a doctoral researcher at the Max Planck Institute for Biogeochemistry and ETH Zürich, focusing on deep learning applications in Earth observation and climate science. As part of the EarthNet project and AI4Carbon Community Initiative , he develops AI systems for climate risk mitigation and carbon cycle modeling. Current Affiliation: ELLIS PhD Student, Max Planck Institute for Biogeochemistry & ETH Zürich Education: MSc Mathematical Physics (University of Leipzig, PIK Potsdam, University of Stockholm, St. Petersburg State University), BSc Mathematics & Business Administration (University of Göttingen) His research interests span: Deep learning for Earth system data Biogeochemical cycle modeling Climate change adaptation strategies Complex systems analysis Early warning systems development Atmospheric transport modeling Analysis of his publications reveals trends in: Scientific machine learning for climate systems Integration of multimodal geospatial data Carbon cycle modeling with neural networks Vegetation forecasting using spatiotemporal methods Climate risk mitigation through AI Teleconnection pattern analysis for weather prediction As a key contributor to the EarthNet initiative , he works on data-driven impact early warning systems for climate extremes. His collaborations include Markus Reichstein and Fanny Yang , with active participation in climate science workshops and symposia.

Moritz Herrmannمشاهده پروفایل

پژوهشگر

• Empirical Machine Learning
• Manifold Learning
• Representation Learning
• +۵ مورد دیگر

Dr. Moritz Herrmann is a postdoc researcher and Reproducibility & Open Science Transfer Coordinator at the Munich Center for Machine Learning (MCML). He is affiliated with the Biometry in Molecular Medicine working group led by Prof. Anne-Laure Boulesteix at Ludwig-Maximilians-Universität München, and contributes to initiatives like the LMU Open Science Center , Open Science Initiative in Statistics (OSIS) , and Open Science Initiative in Medicine (OSIM) . Ph.D. in Statistics from LMU (2022), M.Sc. in Statistics (2018), and B.Sc. in Mathematics/Sports Science (2014) His research focuses on Empirical Machine Learning , Manifold Learning , and Metascience , with emphasis on epistemological foundations and reliability in ML research. He advocates for open science practices and data literacy, as outlined in his ICML 2024 position paper on rethinking empirical ML research. As a member of the Empirical Machine Learning research focus group and the Statistical Learning and Data Science Chair , Herrmann bridges statistical methodology with biomedical applications. His work spans outlier detection, cluster analysis, and reproducibility frameworks, reflected in his recent publications in journals like Biometrical Journal and Data Mining and Knowledge Discovery .

Volker Birkeمشاهده پروفایل

استاد

• Environmental Chemistry
• Groundwater Remediation
• Hazardous Waste Management
• +۳ مورد دیگر

Prof. Volker Birke is a Full Professor of Chemistry/Technical Chemistry at the Wismar University of Applied Sciences, Germany, since 2016. He has over 30 years of experience in environmental chemistry and remediation technologies, with a focus on groundwater and soil contamination. He teaches courses in general, physical, technical, and environmental chemistry for process and environmental engineers. Professor, Wismar University of Applied Sciences (2016–present) Extraordinary Professor, Ostfalia University of Applied Sciences (2009–2016) Research Interests: Specializing in permeable reactive barriers (PRBs) for groundwater pollution, mechanochemical dehalogenation of persistent organic pollutants (POPs), and emerging contaminants like PFAS and pharmaceuticals. His work spans ex situ and in situ remediation technologies for chlorinated ethenes, PCBs, and chromite ore processing residues. International Collaborations: Long-term research partnerships with University of Newcastle and CRC CARE Institute in Australia (since 2004), including student exchanges and joint FuE projects. Also collaborates with Indian Institute of Technology Delhi on groundwater remediation projects. Grants & Projects: Coordinated Germany's large-scale R&D cluster RUBIN for PRB technologies (2006–2012). Currently supervises a DAAD-funded internship in Australia (2024) and a BMWi-funded FuE project (2023–2025) with German companies on wastewater treatment. Labs & Teams: Leads the Chemistry and Environmental Analytics Laboratory in Malchow, Germany, focusing on innovative remediation methods. Has managed over 50 national and international remediation projects in USA, India, and Australia.

• Computational Engineering
• Model-Based Development
• Production Engineering
• +۳ مورد دیگر

Julia Kowalski serves as Professor and Chair of the Department of Methods of Model-Based Development in Computational Engineering at RWTH Aachen University's Faculty of Mechanical Engineering. She holds dual appointments on the Steering Committees for the university's Profile Areas in Production Engineering (ProdE) and Modeling & Simulation Sciences, operating from the Collective Building of Mechanical Engineering in Aachen, Germany. Her research integrates computational engineering with geohazard prediction and cryorobotics, developing advanced numerical methods for multiphysics problems including ice-penetration probes, landslide susceptibility mapping, and wind-energy systems. She pioneers machine learning applications that bridge physical models with engineering design while championing FAIR data principles across cryosphere and geohazard research domains. Current projects focus on model coupling techniques for environmental flows and space exploration technologies. Analysis of her 15 most recent publications reveals dominant trends in surrogate modeling for geotechnical stability, cryorobotic exploration systems, and FAIR data frameworks for environmental science. Her work consistently bridges machine learning with physical modeling across renewable energy, planetary science, and natural hazard mitigation through international collaborations like the TRIPLE project. Scientific awards are not documented in provided materials, though her leadership in DLR-funded space exploration initiatives and editorial roles in topical collections indicates significant recognition. As department chair, she oversees graduate advising and research direction within her computational engineering group, with active grant funding evidenced by German Space Agency collaborations and multi-institutional projects targeting geohazard prediction and cryosphere exploration. Her work demonstrates strong industry-academia-government partnerships. Kowalski leads the Methods of Model-Based Development research group, which operates as an integrated lab for numerical simulation, model coupling, and data-driven engineering solutions. Future work focuses on enhancing uncertainty quantification in geohazard models, advancing cryorobotic technologies for extraterrestrial environments, and developing robust frameworks for FAIR geoscientific data.

Jürgen Scheffranمشاهده پروفایل

استاد

• Climate Change
• Security and Resource Conflicts
• Renewable Energies
• +۴ مورد دیگر

Prof. Dr. Jürgen Scheffran is a Professor of Integrative Geography at the University of Hamburg and leads the Working Group Integrative Geography . His research focuses on the interaction between humans, society, and environment , particularly climate change security risks , resource conflicts , and renewable energy transitions . He specializes in modeling complex systems and technology assessment . Key affiliations : CliSAP Cluster of Excellence, Center for Earth System Research & Sustainability (CEN), International Network of Engineers and Scientists for Global Responsibility (INES) Education : Dr. rer. nat. in agent-based conflict modeling and international security (1986-1989, University of Marburg); Physics studies (1976-1983, University of Marburg) His work bridges climate science and security studies , with a focus on modeling human-environment interactions and conflict resolution in resource-stressed regions . Recent publications analyze climate-induced migration , bioenergy infrastructure , and geoengineering risks . Scientific awards include the Göttinger Friedenspreis (2000) . He has advised on climate policy and nuclear disarmament for institutions like the European Commission , BMBF , and IPCC . Prof. Scheffran actively participates in interdisciplinary research networks , including the DFG-funded CliCCS Cluster of Excellence (2019-2023) and DAAD partnerships on urban energy transitions.

Willi Rathمشاهده پروفایل

پژوهشگر

• Ocean Dynamics
• Marine Data Science
• Lagrangian Methods
• +۷ مورد دیگر

Willi Rath is a Data Scientist at the GEOMAR Helmholtz Centre for Ocean Research in Kiel, Germany, with a focus on Ocean Dynamics. His research bridges marine science and data science, emphasizing Lagrangian methods, air-sea interactions, and biophysical simulations. Rath holds a PhD in Oceanography from Christian-Albrechts-Universität Kiel (2013) and a Diploma in Physics from Humboldt-Universität Berlin (2009). His work spans interdisciplinary collaborations, leveraging machine learning and geospatial analysis to address climate dynamics, disease dispersal in marine ecosystems, and oceanic current variability. Key projects include WarmWorld ELPHE , MarDATA , and OceanParcels , which integrate data science with ocean modeling for climate prediction and marine resource management. Rath’s recent publications highlight his contributions to understanding the Atlantic Meridional Overturning Circulation (AMOC), tropical Atlantic wind forcing, and deep-sea larval connectivity. His methodology incorporates advanced computational tools like echo state networks and metamorphic testing to enhance model transparency and reliability in climate research.

Danijela Dimitrijevićمشاهده پروفایل

پژوهشگر

• Paleontology
• Marine Ecology
• Coral Evolution
• +۳ مورد دیگر

Danijela Dimitrijević is a Postdoctoral Researcher in the Chair of Palaeontology at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). Her work focuses on the evolutionary and ecological dynamics of marine reef communities across deep time, particularly the morphological trait evolution of scleractinian corals and their responses to climate upheavals. Education: PhD in Paleontology (FAU, 2024) International Master in Geosciences: Palaeobiology (FAU, 2021) International Master in Applied Ecology (EMMC-IMAE, 2015) BSc in Geospatial and Environmental Science (University of Belgrade, 2011) Research Interests: Danijela investigates coral reef evolution , trait-based extinction risk , and marine community responses to hyperthermal events . Her projects integrate quantitative trait analysis (e.g., corallite size) with paleoclimatic data to assess long-term ecological resilience. She also co-developed the Ancient Reef Traits (ART) database for global reef-building organism data. Publication Trends: Recent work spans census-based reef carbonate production , bioerosion patterns in Triassic corals , and trait decoupling in extinction events . She emphasizes open science and data equity in paleontological research. Fieldwork & Collaboration: Active in field studies (e.g., Silurian reefs in Sweden) and international collaborations, Danijela bridges geological history with modern conservation challenges through interdisciplinary research and science communication initiatives.

Bernhard Höfleمشاهده پروفایل

استاد

• Geoinformatics
• 3D Point Cloud Analysis
• Environmental Monitoring
• +۵ مورد دیگر

Prof. Dr. Bernhard Höfle is a Professor of Geoinformatics and 3D Geospatial Data Processing at the University of Heidelberg, where he leads the 3DGeo Research Group. His work focuses on computational methods for analyzing 3D/4D point clouds, with applications in environmental monitoring, natural hazard assessment, and vegetation studies. Research Interests: 3D/4D point cloud analysis for environmental observation Development of computational algorithms for geospatial data Integration of machine learning in remote sensing applications Virtual laser scanning simulations (VLS-4D) Real-time monitoring systems for critical infrastructure Digital Twin integration across scales Article Trends: His recent publications emphasize real-time 3D environmental monitoring, virtual simulation of dynamic scenes, multi-temporal analysis of natural processes, and machine learning applications for point cloud classification. They span from 2025 back to 2023 and cover diverse applications including rock glaciers, forest monitoring, building damage assessment, and bee activity tracking. Key Techniques: His work prominently features terrestrial laser scanning, UAV-based LiDAR, and open-source software development. He has contributed datasets like FOR-species20K and developed simulation frameworks such as HELIOS++.