Find Academic Supervisors

• Artificial Intelligence
• Reinforcement Learning
• Internet of Things (IoT)
• +5 more

Dr. Qingzhi Liu is an Assistant Professor at the Information Technology Group within Wageningen University & Research in the Netherlands. He holds a Ph.D. from Delft University of Technology (2016) and postdoctoral research experience at Eindhoven University of Technology (2016-2019). His research focuses on AI-driven solutions for precision agriculture, IoT systems, and edge computing. He teaches courses including Big Data (INF33806), Artificial Intelligence (INF36306), and Advanced Machine Learning (FTE40306). Research interests include reinforcement learning applications in microgrid energy management, deep learning-based crop classification using UAV imagery, and blockchain-enhanced IoT security frameworks. He has published in top venues like IEEE TPDS and IEEE TITS, addressing challenges in smart agriculture, distributed cloud scheduling, and energy-efficient computing. His work integrates computer vision techniques for agricultural monitoring (e.g., plant stem occlusion inpainting), low-code platforms for agri-tech development, and secure IoT data fusion methods. He actively contributes to interdisciplinary projects at the intersection of IT and life sciences.

Gabriele CavallaroView profile

Associate Professor

• Remote Sensing
• Machine Learning
• High Performance Computing
• +3 more

Prof. Gabriele Cavallaro is an Associate Professor at the Faculty of Electrical and Computer Engineering, University of Iceland (since 2024) and concurrently serves as a Visiting Professor at the European Space Agency's Φ-Lab, contributing to the Quantum Computing for Earth Observation (QC4EO) initiative. He leads the Simulation and Data Lab (SDL) at the Jülich Supercomputing Centre (JSC), Forschungszentrum Jülich, Germany, focusing on AI/ML for remote sensing. Previously, he held roles as Deputy Head of the High Productivity Data Processing (HPDP) group (2016–2021), Adjunct Associate Professor at the University of Iceland's Computer Science Department (2022–2024), and Chair/Co-chair of IEEE GRSS technical committees. He earned his B.Sc. and M.Sc. in Telecommunications Engineering from the University of Trento, Italy (2011, 2013), and a Ph.D. in Electrical and Computer Engineering from the University of Iceland (2016). His research emphasizes scalable machine learning algorithms, quantum computing integration, and high-performance computing for remote sensing applications, particularly in geospatial data analysis and earth observation. Prof. Cavallaro has received the IEEE GRSS Third Prize in the Student Paper Competition (2015) . His work bridges academic and industrial collaborations, including leadership in the HDCRS and QUEST TC committees. He has organized workshops and summer schools on disruptive computing architectures and co-edited the IEEE Transactions on Image Processing (TIP) from 2022 to 2024. His roles include advancing modular supercomputing systems like the Juwels Booster and developing tools for distributed neural network training. He is also involved in initiatives such as Sen4map (semantic land-use mapping) and Prithvi (foundation models for geospatial data).

Timo DickscheidView profile

Professor

• Computer Vision
• Biomedical Imaging
• Neuroinformatics
• +3 more

Prof. Dr. Timo Dickscheid is a Professor at Heinrich Heine University Düsseldorf and Working Group Leader for "Big Data Analytics" at the Institute of Neuroscience and Medicine (INM-1) at Forschungszentrum Jülich. His research focuses on developing high-resolution human brain atlases at cellular resolution using methods from Computer Vision, Big Data Analytics, and Neuroinformatics. He leads the Helmholtz AI unit for the research field "Information" and contributes to the European research infrastructure EBRAINS within the Human Brain Project. His work investigates methods for reconstructing 3D brain models at individual cell resolution, developing machine learning techniques for microscopic image analysis, and creating data management systems for terabyte-scale imaging data. Research interests include: Computer Vision for microscopic image analysis Distributed data management for high-throughput imaging Web services for 3D brain data access Machine learning with limited training data His publications show consistent focus on neuroimaging, computational anatomy, and machine learning applications in neuroscience, with recent emphasis on 3D reconstruction, diffusion models, and atlas integration.

Philipp KrapfView profile

Researcher

• Nuclear Medicine
• Radiopharmaceutical Chemistry
• Medical Imaging
• +3 more

Dr. Philipp Krapf leads the Radiopharmaceutical Production Group at Forschungszentrum Jülich’s Department of Nuclear Chemistry (INM-5). His research focuses on developing radiopharmaceuticals for clinical and preclinical applications, particularly in prostate cancer imaging and neurodegenerative diseases. He pioneered the synthesis of [18F]JK-PSMA-7, a PSMA ligand for PET imaging, and has explored automated production methods compliant with Good Manufacturing Practice (GMP). His work also addresses Alzheimer’s pathology through PET-derived markers and investigates the interplay between sleep patterns and synaptic density in human brains. Krapf’s contributions span radiotracer development, radiation safety, and translational nuclear medicine. Key affiliations: Forschungszentrum Jülich, Institute for Neuroscience and Medicine (INM-5) Expertise: Radiopharmaceutical chemistry, PSMA ligand design, PET imaging protocols His research highlights include optimizing radiotracer synthesis workflows, evaluating PSMA ligand variability for targeted therapies, and advancing imaging techniques to detect early-stage Alzheimer’s pathology.

Ralf LämmelView profile

Professor

• Software Language Engineering
• Model-Driven Development
• Semantic Web
• +2 more

Ralf Lämmel is Professor and Head of the Software Languages group at the University of Koblenz. His research spans software language engineering, model-driven development, and semantic web technologies. He specializes in API analysis, data validation (SHACL constraints), and variability management for software systems. Recent work focuses on AI-assisted workflows for archaeology and trust analysis in large language models. Publications demonstrate growing interest in knowledge graph validation and semantic web applications. He has developed tools like the Virtual Platform for software variability and ProGS for property graph validation. Service includes program committee memberships for software engineering conferences and editorial roles. He leads research groups exploring API evolution and RDF validation techniques.

Aneta SchieferdeckerView profile

Senior Lecturer

• Hematology
• Oncology
• Palliative Medicine
• +3 more

Aneta Schieferdecker is a Functional senior physician specializing in Internal Medicine, Hematology and Oncology with a focus on Palliative Medicine at the University Medical Center Hamburg-Eppendorf (UKE). She works within the Center for Oncology at the II. Medical Clinic and Polyclinic, contributing to both clinical practice and research in her specialized fields. Dr. Schieferdecker's research interests span Hematology, Oncology, and Palliative Medicine, with particular expertise in multiple myeloma, end-of-life care, and the challenges of providing palliative care on non-specialized hospital wards. Her work addresses critical gaps in understanding how to improve dying-phase care in general hospital settings and the psychological burden this places on healthcare professionals. Her recent publications demonstrate a strong focus on developing tools and interventions to improve end-of-life care quality, with numerous 2024-2025 publications examining care in the dying phase across different hospital settings. She has also contributed significantly to multiple myeloma research through the GMMG-HD6 clinical trial. Deutsche Gesellschaft für Hämatologie und Onkologie (DGHO) Deutsche Gesellschaft für Palliativmedizin (DGP) Arbeitsgemeinschaft Palliativmedizin (APM) der Deutschen Krebsgesellschaft Dr. Schieferdecker collaborates extensively with colleagues across the UKE and German cancer research networks, particularly on palliative care initiatives and hematological malignancy studies. Her work bridges clinical practice with research to address practical challenges in cancer and palliative care delivery.

Anja LindigView profile

Researcher

• Healthcare Communication
• Patient-Centered Care
• Medical Psychology
• +3 more

Anja Lindig is a Visiting Researcher at the Department of Medical Psychology within the Faculty of Medicine at the University of Hamburg. Her primary affiliation is with the Center for Psychosocial Medicine. Her work focuses on patient-centered communication, shared decision-making in healthcare, and reproductive health. She has contributed to developing and evaluating interventions aimed at improving clinical communication in oncology and other medical settings. Lindig has led mixed-methods studies, including the CarePreg project assessing person-centered care for women facing unintended pregnancy and the stepped-wedge trial evaluating shared decision-making implementation in cancer care. Her research emphasizes translating evidence-based practices into routine clinical workflows, with a particular interest in nurse training and organizational readiness for change. She collaborates with interdisciplinary teams and employs both qualitative and quantitative methodologies. Key areas of expertise include healthcare communication, clinical trial design, and health service evaluation. Education: M.Sc. Neurocognitive Psychology and Dr. rer. biol. hum. (Biological/Health Sciences). Publications span topics like patient empowerment, oncology care quality, and psychosocial aspects of healthcare. Her work bridges research and practice, aiming to enhance patient outcomes through improved communication strategies and organizational interventions.

Selina BaldaufView profile

Researcher

• Dryland hydrology
• Ecohydrological modelling
• Biological soil crusts ecology
• +3 more

Selina Baldauf is a Researcher and Scientific Programmer at the Tietjen Group (Theoretical Ecology) within the Biology Department at Freie Universität Berlin. Her work focuses on ecohydrological modeling and the ecological role of biological soil crusts in dryland ecosystems. She holds a PhD focused on 'Multiscale effects of biological soil crusts on dryland hydrology' as part of the Collaborative Research Centre 973 'Priming and Memory of Organismic Responses to Stress'. Research Interests: Quantifying impacts of biological soil crusts on hydrological patterns Modeling community dynamics under climate change scenarios Evaluating landscape-scale ecological consequences of environmental shifts Her technical contributions include developing computational workflows and IT infrastructure to support large-scale ecological modeling projects. She collaborates with institutions like Fernando T. Maestre's team and actively participates in interdisciplinary research initiatives addressing global change impacts in dryland ecosystems.

• Critical Care Medicine
• Sepsis Pathophysiology
• COVID-19 Therapeutics
• +2 more

Axel Nierhaus is a Professor in the Department of Intensive Care Medicine at University Medical Center Hamburg-Eppendorf. His research focuses on Critical Care Medicine, particularly sepsis pathophysiology, COVID-19 therapeutics, cytokine storm syndrome, and hemoadsorption techniques. His work integrates clinical trials with mechanistic studies to advance critical care therapeutics. Research interests span immunomodulation, biomarker development, extracorporeal blood purification, and precision medicine approaches in critical illness. Key areas include optimizing oxygen therapy in septic shock, steroid protocols, immunoglobulin applications, and hemoadsorption devices. Recent publications demonstrate a strong focus on COVID-19 pathogenesis and management, with studies on vitamin C therapy, RAAS inhibitors, sex-specific outcomes, and cytokine storm characterization. Articles reveal consistent themes in inflammation modulation, organ support strategies, and long-term critical care outcomes. Laboratory and clinical research activities concentrate on molecular pathways of inflammation, clinical trial design, and registry-based evidence generation for blood purification techniques.

• Artificial Intelligence
• Machine Learning
• Space Weather
• +4 more

Dr. Peter Steinbach leads the Group of Artificial Intelligence at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), focusing on interdisciplinary research spanning computational science, space physics, and machine learning applications. His work integrates advanced computing techniques with domain-specific challenges in healthcare, microscopy, and space weather monitoring. Education & Background: While specific educational details are not provided, his research portfolio indicates advanced expertise in physics, computational methods, and software engineering. His career has been centered at HZDR, contributing to major projects like the CLIJ GPU-accelerated image processing framework and space weather studies using ionospheric wave analysis. Research Interests: Steinbach's work emphasizes practical AI applications, including educational tools for machine learning (e.g., "Teaching Machine Learning in 2020") and medical AI systems. His computational science contributions include optimizing high-performance computing for fluid dynamics and parallel processing. In space physics, he investigates VLF transmitter propagation, plasmaspheric dynamics, and geomagnetic storm impacts using satellite data from DEMETER and ground-based networks. Key Projects: Developed CLIJ, a GPU-accelerated image processing tool integrated with Fiji, revolutionizing microscopy workflows. Advanced understanding of ionospheric processes through studies on Schumann resonances and plasmaspheric hiss effects. Pioneered techniques for lightning detection and Q-burst analysis to map Earth's crust conductivity. Technical Contributions: Steinbach has optimized MPI-based fluid dynamics models for GPU systems and created benchmarking tools like gearshifft for heterogeneous computing platforms. His work bridges theoretical research with practical software solutions for the scientific community.

• Earth System Modeling
• Scientific Programming
• Climate Science
• +3 more

Nadine Wieters is a Researcher at the Alfred Wegener Institute (AWI) in Bremerhaven, Germany, specializing in Climate Dynamics. Her primary affiliation is within the Climate Sciences division, focusing on Earth System Modeling and Scientific Programming. She contributes to the development of modular infrastructure for coupled Earth system models through projects like ESM-Tools. Her research emphasizes advancing computational frameworks to enhance model interoperability and efficiency. Key collaborations include work on the AWI-CM model prototype and the esm-interface library. Wieters actively participates in international conferences such as EGU General Assemblies and deRSE events, presenting advancements in software tools for climate research. With a focus on software engineering for climate science, her work bridges computational methods and environmental modeling, supporting both standalone and coupled simulations. She is based at the AWI's Bremerhaven site, contributing to the institute's broader mission in polar and marine research through innovative technical solutions.

• Marine Biology
• Underwater Observatories
• Fish Acoustics
• +3 more

Prof. Dr. Philipp Fischer is a Professor at the Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research, leading the AWI Center for Scientific Diving and the 'In situ ecology' research group. He heads the COSYNA group on underwater observatories and is the AWI-PI for projects ACROSS and MOSES. His work focuses on fish behavior, underwater acoustics, and Arctic marine ecosystems, with a strong emphasis on observational technologies and long-term monitoring. Research interests include the ecological impacts of climate change, kelp forest dynamics, and methane fluxes in marine environments. He coordinates interdisciplinary projects integrating sensor networks and data science platforms like Renku for sustainable marine research. Fischer’s leadership in scientific diving and observatory infrastructure development underscores his role in advancing Arctic and North Sea observational capabilities. Key contributions include the COSYNA coastal observing system and advancements in cabled Arctic observatories. His research bridges biogeochemical processes, technological innovation, and climate adaptation strategies in polar and temperate marine systems.

• Sepsis Research
• Acute Medicine
• Clinical Pharmacology
• +6 more

Prof. Hjalmar Bouma is a Professor in the Faculty of Medical Sciences at the University of Groningen, affiliated with the University Medical Center Groningen (UMCG). He holds roles as an Internist in acute medicine, experimental/clinical pharmacologist, and immunologist. His academic position includes being an Adjunct Professor (Talent Track) in Personalized Acute Medicine since 2023 and Director of the Clinical Pharmacology training program. His research focuses on sepsis pathophysiology, emergency medicine protocols, hibernation biology's translational applications, and biomarker development for acute organ injury. Key contributions include clinical prediction models for sepsis management, electronic nose technology for early sepsis detection, and proteomic studies of sepsis-associated AKI. Prof. Bouma leads the Acutelines biobank initiative, collecting patient data and biological samples from emergency settings. His work bridges experimental models (e.g., hibernation studies in hamsters) with clinical applications in acute care. He has over 100 peer-reviewed publications and collaborates internationally on projects like SUL-138 drug development for sepsis and hydrogen sulfide therapies for organ preservation. His education includes an MD and PhD from UMCG/RuG (2013) with honors, and specialized registrations in clinical pharmacology, immunology, and acute medicine. He actively contributes to medical education through serious game development for ultrasound training and holds an Academic Teaching Qualification.

• Data Science
• Machine Learning
• Distributed Computing
• +4 more

Chris Mattmann is an academic affiliated with the University of Southern California, specializing in data science, machine learning, and high-performance computing. His work spans interdisciplinary applications in Earth sciences, climate modeling, and distributed systems. He has contributed to projects like the Earth System Grid Federation and Apache open-source software. His research emphasizes scalable data management, secure parser development, and automated machine learning systems. Education & Background: While specific educational details aren't listed, his extensive publication record in top-tier journals and conferences indicates advanced training in computer science and computational science. Research Interests: Focus areas include distributed computing infrastructure for scientific applications, machine learning for geosciences, and data-intensive systems. He explores challenges in data movement, scalable supercomputing, and open-source tools for scientific workflows. Publications: Over 150 peer-reviewed articles since 2003, with recent work on super-resolution satellite data, deep learning in geosciences, and secure parser systems. His contributions highlight advancements in both foundational computing and applied environmental science.

Katharina BreiningerView profile

Assistant Professor

• Artificial Intelligence
• Medical Imaging
• Computer Vision
• +3 more

Katharina Breininger is an Assistant Professor (W1) for Artificial Intelligence in Medical Imaging at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), leading the AIMI@FAU Lab within the Department of Artificial Intelligence in Biomedical Engineering. She holds a doctoral degree from FAU's Pattern Recognition Lab, focusing on machine learning and deformation modeling in medical imaging. Her research emphasizes AI applications in interventional imaging and digital pathology, particularly addressing challenges in domain generalization and cross-platform compatibility. Education: PhD in Computer Science (2018), FAU Erlangen-Nürnberg, supervised by Prof. Dr.-Ing. Andreas Maier Research Interests: Computer vision and deep learning for medical imaging Domain adaptation in histopathology and interventional radiology AI-driven diagnostic tools for oncology and cardiology Labs/Teams: AIMI@FAU Lab: Focuses on AI in biomedical engineering and medical imaging workflows Collaborations: Siemens Healthineers: Joint projects on clinical imaging applications Johns Hopkins University: Collaborations on tomographic reconstruction