Find Academic Supervisors

• Geometallurgy
• Compositional Data Analysis
• Geostatistics
• +3 more

Dr. Raimon Tolosana Delgado is a Research Fellow at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), affiliated with the Helmholtz Institute Freiberg for Resource Technology. He leads research in predictive geometallurgy and statistical analysis of mineral resources, focusing on translating geological data into processing insights. His research integrates geostatistics , compositional data analysis (CoDa) , and machine learning to model ore behavior and resource potential. Key areas include: Predictive geometallurgy for forecasting ore/waste behavior Bayesian statistics for parameter estimation and uncertainty analysis Development of R-based tools (e.g., compositions and gmGeostats packages) for mineral data analysis Particle-based process modelling for mineral separation optimization Recent publications emphasize machine learning integration (e.g., neural networks for geophysical tensor fields), tailings reprocessing (3D geostatistical assessment of resource potential), and advanced statistical methods for compositional data. A consistent trend involves enhancing predictive accuracy in mineral processing through multi-source data fusion. Dr. Tolosana Delgado coordinates the development of technology platforms for geometallurgical data analysis, including databases and interfaces for industrial applications. His work bridges ore geology, mineral processing, and metallurgy to optimize resource efficiency.

• MEMS Technology
• Biomedical Microsystems
• Sensors and Actuators
• +5 more

Prof. Dr. Oliver Paul is a Full Professor at the Department of Microsystems Engineering (IMTEK), University of Freiburg, since 1998. He previously held roles such as Dean of Studies (1999–2002), Director of IMTEK (2006–2008), and Dean of the Faculty of Engineering (2016–2018). His research focuses on Microsystems for biomedical applications, MEMS technology, sensor systems, and advanced fabrication techniques. Key projects include the Excellence Cluster BrainLinks-BrainTools (2012–2017) and the Institute for Brain-Machine Interfacing Technology (IMBIT, 2015–present). Paul’s education includes a Diploma in Physics (1986, ETH Zurich) and a PhD in Solid-State Physics (1990, ETH Zurich). He has supervised numerous PhD students and postdocs, contributing to topics like neural probes, energy harvesting, and sensor calibration. His teaching spans courses in MEMS, semiconductors, and quantum mechanics. He co-founded Sensirion (1998) and Atlas Neuroengineering (2012), and holds editorial roles in journals like Sensors and Actuators A and IOP Journal of Micromechanics and Microengineering. His research interests include multisensory system calibration, biohybrid microsystems, and MEMS-based tools for neuroscience. Notable contributions include silicon neural probes, advanced microneedles, and telemetric smart orthodontic brackets. He has led large collaborative projects involving academia and industry, emphasizing interdisciplinary innovation in microsystems and biomedical engineering.

Gaël KermarrecView profile

Researcher

• Boundary Layer Meteorology
• Atmospheric Turbulence
• GNSS Applications
• +3 more

Dr. Gaël Kermarrec is a researcher at the Boundary Layer Meteorology Group , part of the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover . His work focuses on atmospheric turbulence, GNSS applications, and remote sensing for environmental monitoring. Boundary layer meteorology Turbulence theory GNSS signal processing Terrestrial laser scanning Climate change impacts Geodetic time series analysis His research integrates advanced mathematical models like LR B-splines and Matérn covariance with large eddy simulations to study: Atmospheric turbulence effects on optical/GNSS signals Hydrospheric mass loading Deformation analysis of terrain/port infrastructure Climatic sea-level changes Machine learning for remote sensing The 15 most recent articles (2025-2023) demonstrate his focus on: GNSS-based turbulence detection AI-enhanced climate mapping Advanced surface approximation techniques Multi-sensor data fusion Stochastic modeling of geodetic observations Environmental impacts on optical measurements He has developed tools like the Klimascanner QGIS plugin for urban climate resilience and contributes to: Understanding atmospheric scale lengths Improving TLS/GNSS deformation monitoring Analyzing hydrospheric changes Wavefront modeling Ionospheric corrections

Michael ThielView profile

Researcher

• Remote Sensing
• Climate Change
• Agricultural Land Use
• +4 more

Dr. Michael Thiel is a Senior Scientist at the Chair of Remote Sensing at Julius-Maximilians-Universität Würzburg, affiliated with the Philosophische Fakultät and the Department of Remote Sensing within the Institute of Geography and Geology. His academic career includes leadership roles in projects like WASCAL, TelePAtH, and AgriSens DEMMIN 4.0, focusing on climate change impacts in West Africa, remote sensing applications for agriculture, and urban development analysis. He holds a PhD in Remote Sensing from the University of Trier (2013), specializing in SAR data texture analysis for urban area characterization. His research integrates satellite data with geospatial tools to study land use dynamics, climate adaptation strategies, and environmental conservation across Africa and Central Asia. Key research areas include: Climate change impacts on agriculture and urban systems Remote sensing of land cover/land use changes Satellite-based monitoring of soil sealing and impervious surfaces GIS applications for sustainable resource management Decision support systems for climate-resilient agriculture His 2025 publications highlight advancements in spatial decision support systems for West African agriculture, climate adaptation strategies in urban areas, and the use of Earth observations to assess maize cropping systems' vulnerability to climate variability. Recent work also addresses rural industrialization effects on migration patterns and biodiversity conservation. Dr. Thiel is affiliated with organizations like the African Studies Association Germany and serves on the advisory board of the AWARD - One Planet Fellowship. He contributes to international collaborations such as the Climate Change and Land Use Graduate School at Kwame Nkrumah University of Science and Technology (Ghana). His research outputs include over 50 peer-reviewed articles since 2007, focusing on remote sensing techniques for environmental monitoring, urban dynamics analysis, and agricultural sustainability in developing regions.

Claas NendelView profile

Professor

• Agroecosystem Modelling
• Climate Change Impact Assessment
• Crop Simulation
• +3 more

Prof. Claas Nendel is a Professor at the Leibniz Centre for Agricultural Landscape Research (ZALF), leading the Ecosystem Modelling and Landscape Modelling groups within Research Area 4 'Simulation and Data Science'. His work focuses on agroecosystem dynamics, climate change impacts, and crop modelling integration with remote sensing data. Research interests include: Simulation of crop-water-nutrient interactions Climate adaptation strategies for European agriculture Remote sensing applications in agricultural monitoring He has pioneered the MONICA model for agroecosystem simulation and contributed to AgMIP global crop modelling initiatives. Recent studies emphasize soybean and wheat system resilience under climate change, irrigation mapping via satellite fusion, and drought risk assessments. Awards include his role as Past ESA President (European Society for Agronomy). Key projects involve: Intercropping systems optimization Grassland mowing detection via Sentinel time series Soil organic matter dynamics under no-till practices His work bridges field experimentation with large-scale modelling to address agricultural sustainability challenges.

• Antimicrobial Resistance (AMR)
• One Health
• Sensor Technology
• +7 more

Tina Kabelitz is a Researcher at the Leibniz-Institut für Agrartechnik und Bioökonomie e.V. (ATB) , leading the working group Infections and AMR in Farm Animals since September 2021. Her work integrates sensor technology, digitalization, and artificial intelligence to address antimicrobial resistance (AMR) in livestock systems through a One Health approach. Education : PhD in Epigenetics of Plants (University of Potsdam, 2011-2015) MSc Molecular and Cellular Biology (University of Potsdam, 2009-2011) BSc Molecular Biology/Physiology (University of Potsdam, 2006-2009) Her research focuses on antimicrobial resistance dynamics in livestock, particularly through environmental pathways in pig and poultry farming. She investigates resilient housing design for biosecurity and animal welfare, microbiome changes in manure storage , and machine learning-based mastitis risk assessment using multisensor systems. Her projects include LeibnizLabPP (pandemic preparedness), AirBarn (bioaerosol AMR transmission), ENVIRE (AMR control in chicken systems), and AMR-PIG (AMR spread mechanisms). Recent publications analyze AMR gene dynamics in manure systems (2025), machine learning applications for mastitis prediction (2025), and environmental impacts of dairy farming mitigation strategies (2024). Her work emphasizes systemic solutions like anaerobic digestion plants and slurry injection techniques to reduce emissions.

• Surface Metrology
• Additive Manufacturing
• Precision Engineering
• +4 more

Professor Jane Jiang is a distinguished academic at the University of Huddersfield , affiliated with the School of Computing and Engineering and the Department of Engineering . She specializes in surface metrology, precision engineering, and advanced manufacturing technologies. Her work focuses on optimizing measurement techniques for additive manufacturing, X-ray computed tomography, and optical systems. Research Interests Her research spans surface texture analysis , 3D vision systems , and metrology for smart manufacturing . She develops novel methods for characterizing complex surfaces using techniques like phase measuring deflectometry and chromatic confocal sensors. Her contributions bridge engineering and materials science, emphasizing practical applications in aerospace, biomedical, and industrial sectors. Recent Contributions Her 2025 work includes advancements in freeform optics design, vibration-resistant microscopy, and neural network-based surface characterization. Her studies on XCT measurement for additive manufacturing parts address critical challenges in precision and data analysis. Affiliations & Projects She leads the Centre for Precision Technologies and actively collaborates on EU-funded projects. She organized the 1st International Conference on Metrology and Standard (2024) and contributes to interdisciplinary initiatives in bio-engineering and advanced materials. Grants & Teams Her team includes researchers like Shan Lou and Paul Scott , focusing on metrology for medical implants and aerospace components. She supervises PhD students exploring AI-driven surface specification and manufacturing process optimization.

• Remote Sensing
• Geoinformatics
• Environmental Monitoring
• +3 more

Prof. Katharina Anders holds the Professorship for Remote Sensing Applications at the Technical University of Munich (TUM), within the TUM School of Engineering and Design. Her research focuses on methodological advancements in remote sensing data analysis, particularly for observing Earth surface dynamics in contexts like natural hazards, climate change, and human-environment interactions. She earned her PhD in Geoinformatics from Heidelberg University, with a research stay at TU Delft. Key contributions include 4D observation techniques for geo-environmental monitoring and automated change detection systems using LiDAR and photogrammetry. Education: Studied Geography, Computer Science, and Environmental Physics at Heidelberg University Doctorate in Geoinformatics (cum laude) from Heidelberg University Research stay at TU Delft Research interests emphasize cross-scale analysis of landscape dynamics, with a focus on integrating high-resolution 3D/4D time series and machine learning for environmental monitoring. Her work bridges remote sensing, geoinformatics, and environmental science, addressing challenges like coastal erosion, permafrost thaw, and infrastructure safety. Her award-winning papers and projects include advancements in LiDAR-based change detection, fusion of multisource data, and open educational resources like the E-TRAINEE platform for time series analysis. Recent publications highlight innovations in 4D object segmentation, UAV photogrammetry, and snow cover dynamics tracking. Notable awards include the Baden-Württemberg Foundation Fellowship (2022) and the ISPRS Geospatial Week Best Paper Award (2019). Her research has been applied to diverse case studies, from Arctic permafrost monitoring to Tyrolean rockfall prevention.

• Bioacoustics
• Bird Conservation
• Ecology
• +3 more

Dr. Olaf Jahn is a Research Associate at the Museum of Natural History Leibniz Institute for Evolution and Biodiversity Research in Berlin, Germany. His work focuses on bioacoustic methods for biodiversity monitoring through the AMMOD project (Automated Multisensor Stations for Monitoring of Biodiversity) as part of Module 5: Automated Bioacoustic Monitoring. Research Focus: Bioacoustics, rapid detection of bird communities, ecological monitoring, and conservation biology Key Tools: Automated sound recognition systems, spectrogram analysis, temporal contextual integration algorithms His research spans tropical avian ecology in regions like Ecuador's Chocó forest while developing cutting-edge signal processing approaches for acoustic monitoring. Jahn's work bridges classical field biology with modern computational methods, creating robust tools for real-world biodiversity assessment. Through publications in journals like PLOS One, Applied Acoustics, and Expert Systems with Applications, he has advanced automated bird identification techniques using: Morphological filtering of spectrograms Temporal contextual information integration Direction of arrival estimation with microphone arrays Robust frame selection for audio parameterization Acoustic detection of endangered species

• Human Factors Engineering
• Automated Driving
• Driver State Modeling
• +2 more

Jonas Radlmayr is a Researcher at the Department of Ergonomics, Technical University of Munich, where he has been actively contributing since 2014 under Prof. Klaus Bengler's Chair. His work focuses on human factors in automated driving systems, with emphasis on driver-state modeling and transition-of-control mechanisms. Education: Diplom-Ingenieur (Master's equivalent) in Mechanical Engineering, Technical University of Munich (2013), specializing in Control Engineering and Ergonomics Radlmayr's research investigates critical human-automation interaction challenges in conditionally automated driving (Level 3). His work examines how driver drowsiness, non-driving tasks, and system reliability impact take-over performance during transitions of vehicle control. He has pioneered methodological frameworks including the Take-Over Performance Score (TOPS) for quantifying driver readiness and developed integrative models for drowsiness progression in automated contexts. His experimental designs address real-world challenges in human-machine interface validation and driver monitoring system development. Analysis of his 15 most recent publications reveals consistent focus on driver availability metrics, human factors in cut-in scenarios, and mental model formation regarding automation levels. The research demonstrates strong interdisciplinary connections between cognitive psychology, transportation engineering, and physiological monitoring, with recurring themes in usability evaluation frameworks and safety-critical transition protocols. As an integral member of TUM's Ergonomics Chair, Radlmayr utilizes advanced research infrastructure including the Dynamical Mock-Up, Modular Ergonomic Mockup, and Static Driving Simulator. His collaborative work with industry partners addresses practical implementation challenges in automated vehicle design while advancing theoretical understanding of human-automation teaming in transportation systems.

Peter-Michael KaulView profile

Professor

• Sensor Technology
• Explosives Detection
• Chemical Sensors
• +7 more

Prof. Dr. Peter-Michael Kaul is a Professor of Physics, Statistics and Measurement Technology at the University of Applied Sciences Bonn-Rhein-Sieg (H-BRS), where he serves as Research Professor and Founding Director of the Institute for Security Research (ISF). He is also a member of the University Council and the Research Commission of H-BRS. His academic career spans over two decades at the university, with significant leadership roles including Vice Dean of the Department of Biology, Chemistry and Materials Engineering (1999-May 2002), Prorektor für Lehre (Vice President for Teaching and Studies) (May 2002-Oct 2005), and Dean of the Department of Applied Natural Sciences (Oct 2005-Nov 2007). Since November 2010, he has been deputy director of the Institute for Detection Technologies, and since January 2011, he has served as Director of the Institute for Security Research. Prof. Kaul's research interests focus on: Sensor technology and actuation systems Microsensors and mass-sensitive sensors Chemical and biosensors for gaseous and liquid media Sensor signal processing and multisensor systems Intelligent sensor systems Explosives detection technologies and Counter-IED methods Laser drilling and gas analytics for security applications Instrumental analytics for explosive and odor component detection His recent publications demonstrate a strong focus on advanced sensor technologies for security applications, particularly in the detection of explosives and hazardous materials. His work spans from fundamental sensor development (such as semiconductor gas sensors, Raman spectroscopy, and SERS substrates) to practical applications in security screening and explosives detection. A significant portion of his research involves the development of specialized detection systems for triacetone triperoxide (TATP) and other energetic materials, often using innovative approaches like laser initiation and acoustic monitoring. His team also works on improving the reliability of explosives detection dogs and developing algorithms for sensor data validation. Prof. Kaul has received funding for numerous research projects, including: TeamUP: Addressing CBRN-E events (Chemical, Biological, Radiological, Nuclear, and Explosive hazards) DigitalTwin-4-Multiphysics-Lab: Urban digital twins and multiphysics twins for industry NAkSU: New analysis methods for complex security and environmental data WireLife: Lifetime of new aluminum wires in power electronics SYNergie: Detection and inactivation of Synchytrium endobioticum in potatoes ReDeX: Reductive treatment method for removal of disinfection by-products from drinking water PräventinS: Prevention strategy for invasive pests like the Asian longhorned beetle ALBERO: Safe integration of alternative vehicles in roll-on/roll-off ferry traffic FHInvest: Field emission electron microscope with computed tomography for materials development Prof. Kaul leads the Institute for Security Research and has been instrumental in establishing security research as a key focus area at H-BRS. His work bridges fundamental sensor research with practical security applications, particularly in the areas of explosives detection and counter-terrorism technologies. He collaborates extensively with industry partners, government agencies, and international research institutions to develop innovative security solutions.

Daniel SonntagView profile

Professor

• Intelligent User Interfaces
• Natural Language Processing
• Information Retrieval
• +7 more

Prof. Dr.-Ing. Daniel Sonntag serves as Scientific Director of Interactive Machine Learning at the German Research Center for Artificial Intelligence (DFKI) and holds an Endowed Chair of Applied Artificial Intelligence at University of Oldenburg. He leads multiple significant research initiatives including No-IDLE (Interactive Deep Learning Enterprise), MASTER (Mixed reality ecosystem for teaching robotics), Ophthalmo-AI (intelligent eye care diagnostics), pAItient (AI applications in medicine), and XAINES (explainable AI with narratives). His research spans intelligent user interfaces, natural language processing, information retrieval, dialogue systems, common-sense modeling, and explainable machine learning methods for cognitive computing. His work particularly focuses on multimodal-multisensor interfaces for medical and health systems and Industry 4.0 applications. His research has had industrial impact, with SmartWeb's multimodal interaction design forming the technical infrastructure for Apple's Siri. His recent publications demonstrate strong trends in medical AI applications, particularly in eye tracking, radiology report generation with enhanced interpretability, and interactive federated learning frameworks. His work consistently bridges theoretical AI advances with practical medical and industrial applications. German High Tech Champion Award (2011) As a principal investigator, Sonntag leads national and European projects funded by the Federal Ministry of Education and Research, Federal Ministry for Economic Affairs and Energy, Federal Ministry of Health, and Horizon Europe. He has supervised numerous researchers including Michael Barz, Jan Zacharias, and Alexander Prange across multiple projects. His research group has successfully transitioned technologies to industry, with former researchers joining companies like Apple, Wacom, and Semvox. Sonntag's laboratory work centers on the Interactive Machine Learning department at DFKI, with strong connections to medical institutions and industry partners. His team develops cognitive assistance systems, mixed reality applications for smart factories, and AI-based diagnostic support tools, particularly in ophthalmology and radiology.

Sabine ChabrillatView profile

Professor

• Hyperspectral Remote Sensing
• Environmental Science
• Geosciences
• +7 more

Prof. Dr. Sabine Chabrillat serves as Professor for "Digital Soil Mapping" at Leibniz University Hannover and the GFZ German Research Centre for Geosciences . She leads the Applied Hyperspectral Remote Sensing working group at GFZ and is the scientific principal investigator for the German EnMAP hyperspectral satellite mission. Academic affiliation: Faculty of Natural Sciences, Department of Soil Science Research focus: Hyperspectral remote sensing, soil spectroscopy, soil erosion/degradation, polar ecosystems, and arid region monitoring Her work spans multiscale analyses from laboratory measurements to satellite-based systems, with projects in Southern Africa, Brazil, the Mediterranean, and Antarctica. Recent studies include: EnMAP satellite validation for soil/mineral mapping Thermal hyperspectral analysis of heatwave impacts Global soil spectral grid development Polar snow reflectance modeling Scientific awards include the GFZ Prize (2019), Lifetime Achievement Award (2017), and WHISPERS Best Paper (2018). She has authored >220 publications and co-chaired the IEEE P4005 working group on soil spectroscopy standards since 2020. As a dedicated educator, she lectures in remote sensing at Leibniz University Hannover , University of Potsdam , and Free University Berlin , and mentors PhD/MSc students in soil and environmental sciences.

• Augmented Reality
• Mixed Reality
• Sensor Networks
• +3 more

Nicole Huber is a researcher at the Chair for Computer Aided Medical Procedures & Augmented Reality at Technische Universität München (TUM), affiliated with the Institute for Informatics I16. Her work focuses on augmented reality systems, sensor networks, and human-computer interaction. Broad research areas: Augmented Reality, Mixed Reality, Sensor Networks, Human-Computer Interaction Key projects: Gestyboard, SensorVis, OST Rift, Parasitic Tracking Research trends from her publications reveal expertise in temporal calibration , multisensor tracking , mobile AR , input device design , and ubiquitous tracking environments . She has contributed to 3D visualization, gesture recognition, and RFID-based location computing. Scientific award: Best short paper award at 11th Symposium on Virtual and Augmented Reality (SVR), Porto Alegre, Brazil, May 2009 Teaching includes courses like "Medical Augmented Reality" and "Computer Aided Medical Procedures" at TUM. She has advised numerous publications and projects in AR systems.

Birgit KleinschmitView profile

Professor

• Remote Sensing
• Vegetation-Soil Interactions
• Environmental Planning
• +3 more

Prof. Dr. Birgit Kleinschmit is a Professor at the Technical University of Berlin specializing in Geoinformation in Environmental Planning and serves as Principal Investigator for the DFG Research Group FOR 2694 "Cosmic Sense", leading its Remote Sensing (RS) module focused on hydrological and environmental applications. Her research centers on spatio-temporal dynamics of vegetation-soil interactions using multi-temporal and multisensor remote sensing technologies, with extensive interdisciplinary collaboration across national and international projects. Key research domains include: Advanced remote sensing for environmental monitoring Land surface process modeling Spatio-temporal geospatial analysis Interdisciplinary environmental planning Prof. Kleinschmit actively contributes to major funded initiatives including: DFG: UWI (Urban Water Interfaces) DFG: UCaHS (Understanding Changes in the Hydrological System) BMBF: SuMaRiO (Sustainable Management of River Oases) INNOVATE (Innovative Water Management Technologies) These projects emphasize sustainable resource management through integrated geospatial approaches. No scientific awards were documented in the source material. Her advisory roles remain unspecified, though her leadership in consortium-based research demonstrates significant collaborative grant management. As core faculty within the Cosmic Sense team, she operates within the Remote Sensing module that integrates satellite and airborne data across multiple field sites to advance cosmic-ray sensing applications in hydrology.