جست‌وجوی استاد و پژوهشگر

• RNA Folding
• Computational Biology
• Bioinformatics
• +۳ مورد دیگر

Stefan Badelt holds a PhD and Magister degree in Theoretical Chemistry, currently active within the Faculty of Chemistry at an Austrian institution (likely University of Vienna based on academic conventions). His research spans computational RNA biology, bioinformatics, and molecular engineering with continuous publication output from 2011 through projected 2025 activities. RNA Folding (100%) Protein Secondary Structure (67%) Transcription Processes (45%) Circular RNA Mechanics Alu Element Biology His research focuses on cotranscriptional RNA folding dynamics, developing computational tools like DrForna for visualization and DrTransformer for heuristic folding prediction. Recent work critically examines deep learning limitations in RNA structure prediction while advancing nearest-neighbor energy models. His fingerprint reveals strong specialization in RNA polymerase II dynamics and non-pseudoknotted structural systems. Publication trends show consistent output in high-impact bioinformatics journals, with 2022-2023 featuring significant algorithmic contributions. His work bridges computational methods with experimental validation, particularly in RNA folding pathways and DNA strand-displacement systems. While no specific awards are documented, his publications demonstrate substantial scholarly impact with multiple papers exceeding 10 Mendeley readers and significant citation metrics. Badelt actively mentors students through collaborative projects, evidenced by co-authored publications with Laurenz Miksch and others. His research receives support through computational biology grants enabling development of tools like the commit-and-delay model for RNA dynamics. Future work includes domain-level design of cotranscriptional folding pathways presented at upcoming 2024-2025 conferences. He operates within the Theoretical Chemistry research ecosystem, collaborating closely with Ivo Hofacker's group on RNA bioinformatics. Current activities focus on exact coarse-grained dynamics modeling and pseudoknot-free pathway design, positioning his lab at the intersection of computational biology and molecular engineering.

Jiri Hladuvkaمشاهده پروفایل

پژوهشگر

• Computer Vision
• Medical Image Analysis
• Machine Learning
• +۳ مورد دیگر

Projektass. Dr.techn. Jiri Hladuvka is a PostDoc Researcher at Vienna University of Technology, affiliated with the Mixed Reality Lab. His research focuses on computer vision, medical image analysis, and machine learning applications in healthcare. Key areas include image segmentation, graph-based algorithms, and topological data analysis. Education: Holds a Dr.techn. (Doctor of Engineering) degree. Research Interests: His work spans medical imaging analysis using deep learning, validation of image segmentation techniques, and visualization systems for neurobiology. He explores methods like Local Binary Patterns, graph contraction, and outlier detection to improve accuracy in medical diagnostics and image processing. Publications: Recent works include advancing early osteoarthritis detection via texture features, validating OCT scan segmentations, and developing visualization tools for neurobiological data. His contributions highlight interdisciplinary approaches in computer science and biomedical applications. Labs/Teams: Active in the Mixed Reality Lab, focusing on innovative visualization and data analysis solutions.

• Materials Physics
• Thin Films
• Mechanical Engineering
• +۵ مورد دیگر

Juraj Todt is a Professor and Chair of Materials Physics, specializing in advanced materials characterization and mechanics. His research focuses on thin films, nanomaterials, and energy materials, with a strong emphasis on understanding microstructural and mechanical properties through techniques like X-ray diffraction and in situ experiments. He has conducted extensive studies on topics such as thermomechanical fatigue, hydrogen embrittlement, and solid-state battery materials. His work often involves collaborations with leading institutions like DESY and ESRF, where he has engaged in visiting research and presented findings. Key areas of interest include the interplay between microstructure, stress, and material performance, particularly in contexts like battery safety, superelastic alloys, and nanoscale stress analysis. Recent contributions highlight advancements in machine learning for material property prediction, as well as insights into polysulfide dynamics in Na-S batteries and lithium dendrite suppression strategies. His research bridges fundamental materials science with practical applications in energy storage, aerospace, and mechanical engineering.

• Computer Vision
• Optimization Methods
• Medical Imaging
• +۵ مورد دیگر

Thomas Pock is a Professor at the Institute of Visual Computing at TU Graz. His research focuses on computer vision, optimization methods, and mathematical models in computer vision, with significant contributions to medical imaging and inverse problems. He leads projects integrating deep learning with traditional variational methods for applications in MRI reconstruction, microscopy, and cardiac signal analysis. His work emphasizes efficient sampling techniques, uncertainty quantification, and algorithmic optimization in computational imaging. Education and academic background are not explicitly detailed in the provided texts, but his extensive publication record indicates expertise in interdisciplinary areas such as biomedical engineering, signal processing, and machine learning. Key research themes include variational networks, total variation methods, and generative adversarial networks (GANs) for image reconstruction and segmentation. His research spans collaborations in both academic and clinical settings, addressing challenges in 3D reconstruction, particle flow estimation, and cardiac electrophysiology modeling. Notable projects include Total Deep Variation and inverse Eikonal methods for medical data analysis. He actively contributes to open-source tools and frameworks for variational optimization and deep learning integration.

• Security and Privacy
• Reverse Engineering
• Machine Learning
• +۵ مورد دیگر

Michael Pucher is a dedicated researcher affiliated with the Faculty of Computer Science at Vienna University of Technology, where he contributes to the Research Group Security and Privacy. Currently serving as a Visiting Researcher (October 2023 to February 2024), his work bridges academic research and practical security challenges in computing systems. He holds a Diplom-Ingenieur (Dipl.-Ing.) degree, equivalent to a Master of Science in Engineering, and a Bachelor of Science (BSc), reflecting a strong foundation in technical disciplines. Dr. Pucher's research spans the critical domains of computer security and privacy, with a focus on reverse engineering, obfuscation techniques, binary analysis, and the application of machine learning to security problems. His investigations delve into integrated circuit analysis, software protection mechanisms, and the development of resilient methods for identifying semantic functionality in obfuscated programs. Analysis of his 2022 publications reveals a cohesive research trajectory emphasizing innovative solutions for security challenges. Key themes include the integration of machine learning for clone detection in binaries, simulation-based approaches to counter obfuscation, and advanced image processing techniques for hardware reverse engineering, indicating a multidisciplinary approach that merges hardware and software security. Active in the academic community, Dr. Pucher has contributed through teaching engagements, peer review for conferences such as the IEEE Workshop on Offensive Technologies, and presentations at international venues including the IEEE Physical Assurance and Inspection of Electronics (PAINE) workshop.

Edvinas Stankunasمشاهده پروفایل

پژوهشگر

• Nuclear pore complex structure
• mRNA export mechanisms
• Structural biology
• +۳ مورد دیگر

Edvinas Stankunas, MSc, is a researcher at the Max Perutz Labs of the Medical University of Vienna. His work focuses on the nuclear pore complex (NPC), specifically the structural and functional organization of the nuclear basket. Current research explores how the basket docks onto the NPC core using AlphaFold-based interaction screens and electron microscopy. His findings elucidate how linear motifs and protein junctions reshape NPC architecture, impacting mRNA export and quality control. Education: Advanced studies in Molecular Biology (MSc) Research Interests: Mechanisms of nuclear transport Structural biology of the NPC Protein-protein interaction dynamics Membrane-anchored protein complexes Articles Trends: Recent work emphasizes integrative structural approaches, leveraging AI tools like AlphaFold to model NPC components. Key contributions include defining the tripartite junction between basket and NPC core, and阐明membrane-anchored assembly principles. Grants/Advising: No specific grants or advisees listed. Collaborates with Alwin Köhler and other structural biologists. Labs/Teams: Associated with the Molecular Biology division at Max Perutz Labs, part of the Medical University of Vienna's research ecosystem.

Filipa Sousaمشاهده پروفایل

استادیار

• Microbial Evolution
• Archaea Physiology and Ecology
• Metabolic Evolution
• +۳ مورد دیگر

Dr. Filipa Sousa serves as Assistant Professor in the Department of Functional and Evolutionary Ecology at the University of Vienna's Faculty of Life Sciences, leading the Filipa Sousa Lab within the Archaea Biology and Ecogenomics Unit. Her research program integrates genomic, phylogenetic, and experimental approaches to investigate microbial evolution with emphasis on archaeal physiology, metabolic innovation, and bioenergetic transitions across Earth's history. The lab operates from room 3.042 at Djerassiplatz 1 in Vienna. Her primary research interests focus on the evolution of microbial metabolic strategies, particularly energy conservation mechanisms in Archaea. She investigates how carbon and energy metabolic systems evolved through protein complex modularity, gene fusions, and large-scale comparative genomics. Current work emphasizes automatic metabolic classification from genomic data, pan-metabolic profiling of Archaea, and reconstructing evolutionary pathways for sulfur and electron transport systems. Her group combines phylogenomics with experimental validation to bridge geological records and microbial physiology. Analysis of her recent publications reveals dominant trends in archaeal metabolism evolution, particularly dissimilatory sulfur reduction pathways and respiratory complex assembly. Her work increasingly integrates metagenomic data with phylogenetic modeling to reconstruct ancestral metabolic states, while developing computational tools for metabolic classification. Key themes include electron bifurcation mechanisms, horizontal gene transfer in metabolic innovation, and geochemical constraints on early bioenergetic systems. Major scientific recognition includes: ERC Starting Grant (2019-2025) for "Evolution of Physiology: The link between Earth and Life" WWTF Vienna Research Group Grant (2016-2025) for "Pan-metabolic profiling of Archaea: The Ecology of Genomics" Dr. Sousa actively supervises five graduate students across PhD and Master's programs while leading a 12-member research team. Her Vienna Doctoral School project "Microbial biotransformations in biogeochemical cycles" examines metal-based energy conservation in environmental microbes. She maintains significant collaborations with William F. Martin (Heinrich-Heine-Universität Düsseldorf) and Christa Schleper (University of Vienna), with funding supporting experimental work, computational analyses, and field studies in extreme environments. The Filipa Sousa Lab comprises Anwar Hiralal (PhD), Jordi Zamarreno Beas (PhD), Val Karavaeva (M.Sc.), Anastasiia Padalko (M.Sc.), Constantin Leitgeb (B.Sc.), and Marta Medic (B.Sc.). The team operates within the Archaea Biology and Ecogenomics Unit under Christa Schleper's departmental leadership, utilizing advanced genomic and bioinformatic infrastructure. Current projects integrate metagenomic data from diverse environments with phylogenetic modeling to reconstruct metabolic evolution, with particular focus on uncultivated archaeal lineages and their ecological roles.

Michele Reticcioliمشاهده پروفایل

پژوهشگر

• Quantum Materials
• Computational Materials Physics
• Surface Science
• +۴ مورد دیگر

Michele Reticcioli is a researcher in quantum and computational materials physics, currently affiliated with CNR L'Aquila, Italy, after previously serving at the University of Vienna until 2024. His work focuses on polarons, surface science, and machine learning applications in materials discovery. He leads research groups exploring electronic properties of materials using advanced computational methods like density functional theory (DFT) and has contributed to understanding polaron dynamics, surface reconstructions, and catalytic mechanisms on oxide surfaces. Teaching includes courses on computational quantum mechanics, data science for physicists, and surface science at the University of Vienna, emphasizing practical simulations using tools like VASP. He has supervised numerous students in topics like machine learning for polaron modeling, surface defect analysis, and computational materials physics. His research has been published in top journals such as Nature Communications , Science Advances , and Physical Review Letters , with a focus on interdisciplinary approaches combining theory and experiments. Key areas include polaron-driven surface phenomena, thermoelectric materials, and superconductivity in hydrides.

Horvath Peterمشاهده پروفایل

استاد

• Computational Biology
• Artificial Intelligence
• Microscopy
• +۳ مورد دیگر

Peter Horvath is a distinguished academic and researcher in computational biology and artificial intelligence. He currently serves as Principal Investigator at the AI4Health Institute of Helmholtz Munich, Germany, and Director of the Institute of Biochemistry at the Biological Research Centre (BRC) in Szeged, Hungary. He holds a DSc from Hungary, a PhD in Digital Image Analysis from the University of Nice, and an MSc in Computer Science and Mathematics from the University of Szeged. His research focuses on bioimage informatics, AI-driven biological discovery, and single-cell analysis. Horvath has led major initiatives, including the Finnish Distinguished Professor Fellowship at FIMM (University of Helsinki) and founded Single-Cell Technologies Ltd. He is a prolific grant recipient, securing funds for projects like 'AI-driven machines for biological discoveries' and 'Proteogenomic map of tumor cell division'. His work bridges computational methods with biological applications, notably developing tools like nucleAIzer and Deep Visual Proteomics. Key recognitions include the Szent-Györgyi Talentum Prize (2019), Momentum Award (2018), and Finland Distinguished Professorship (2014). He actively contributes to scientific communities, organizing conferences like NEUBIAS 2018 and serving on boards for the Society of Biomolecular Imaging.

• Organic Chemistry
• Crystallography
• Materials Science
• +۳ مورد دیگر

Kiyofumi Takaba serves as an Academic third-party funded project staff in the Department of Inorganic Chemistry at the Faculty of Chemistry. His research focuses on advanced structural analysis techniques, including electron crystallography, XFEL microcrystallography, and cryo-EM methodologies. His work bridges organic chemistry and materials science, addressing topics such as organic semiconductor design, peptide self-assembly, and polymorphic phase transitions. Key areas of expertise include: Development of novel synthetic routes for complex organic molecules High-resolution structural characterization of biomolecules and materials Application of cutting-edge instrumentation like X-ray free-electron lasers (XFEL) and cryo-electron microscopy Recent contributions include studies on: Conformational polymorphism in organic semiconductors Bottom-up peptide nanoshape design Chiral-induced spin selectivity in supramolecular systems His research collaborations involve advanced facilities such as the RIKEN SPring-8 Center, leveraging synchrotron and XFEL resources for structural studies. While no formal awards are listed, his publication record reflects significant contributions to crystallography and materials chemistry. Lab affiliations include teams working at the interface of chemistry and physics, with a focus on developing hybrid methodologies for molecular structure determination.

Thomas Grafمشاهده پروفایل

استاد پژوهشی

• Macrophage
• Cancer biology
• Reprogramming
• +۱۶ مورد دیگر

Thomas Graf is a Research Professor at the Centre for Genomic Regulation (CRG) in Barcelona, Spain, and an ICREA Research Professor since 2006. His academic career spans multiple prestigious institutions including Albert Einstein College of Medicine, European Molecular Biology Laboratory, and Max Planck Institute. Research focus: Cancer biology, Reprogramming, Genetic engineering, Molecular biology Key contributions: Hematopoietic stem cells, Transdifferentiation, Computational biology His recent publications demonstrate expertise in inverse imaging problems, diffusion models, and biomedical applications spanning cardiology and protein structure analysis. Scientific accolades include the Paul Ehrlich Prize and membership in Academia Europaea. 1989: Paul Ehrlich and Ludwig Darmstaedter Prize 1983: Wilhelm Warner Foundation Prize Other: Leukemia Research Award, German Society for Microbiology and Hygiene Prize, Academia Europaea membership

Peter Horvathمشاهده پروفایل

پژوهشگر

• Computational biology
• Artificial Intelligence
• Microscopy
• +۵ مورد دیگر

Peter Horvath is a Senior Researcher at Helmholtz Munich's AI4Health Institute and Director of the Institute of Biochemistry at HUN-REN Biological Research Centre (BRC) in Szeged, Hungary. He also leads the Biological Image Analysis and Machine Learning Group (BIOMAG) at BRC since 2014 and serves as CEO of Single-Cell Technologies Ltd. in Szeged. Education: DSc. (2023, Hungary) Ph.D. in Digital Image Analysis (2008, University of Nice, Sophia Antipolis, France) MSc in Computer Science and Mathematics (2003, University of Szeged, Hungary) Research Interests: His work integrates computational biology, artificial intelligence, and machine learning with bioimage analysis and single-cell technologies. Key areas include cancer research, proteogenomics, and the development of AI-driven systems for biological discovery and clinical applications, particularly in tumor cell division and neurobiology. Scientific Honors and Fellowships: He has received notable awards such as the Szent-Györgyi Talentum Prize (2019), Straub and Bolyai Plaquette Awards (2018), and the Momentum Award from the Hungarian Academy of Sciences (2018). Fellowships include the Finland Distinguished Professor program (2014), Marie Curie Fellowship (2007), and Bolyai Fellowship (2014). Research Funding: His projects have secured major grants from the Hungarian Academy of Sciences (HAS Momentum), Chan Zuckerberg Initiative (Deep Visual Proteomics), ELKH Excellence Programme, OTKA SNN, NRDIO, EU Horizon 2020, and TRANSCAN2021, focusing on AI-based diagnostics, single-cell analysis, and advanced microscopy techniques.

Nassir Navabمشاهده پروفایل

استاد

• Computer Assisted Interventions
• Augmented Reality
• Medical Imaging
• +۳ مورد دیگر

Prof. Dr. Nassir Navab is a full professor and director of the Chair for Computer Aided Medical Procedures (CAMP) at the Technical University of Munich (TUM), Germany. He also serves as an adjunct professor of computer science at Johns Hopkins University (USA) and holds secondary appointments at TUM’s Medical School. He is internationally recognized for his pioneering work in computer-assisted interventions, augmented reality, medical imaging, computer vision, and machine learning. Education: PhD from INRIA and University of Paris XI, France Postdoctoral Fellowship at MIT Media Laboratory Research Interests: Prof. Navab's research bridges the gap between computer science and medicine. His core interests include: Robotic Imaging Systems : Developing robotic platforms for intraoperative imaging Augmented Reality in Surgery : Creating AR systems for surgical navigation Medical Image Computing : Advanced algorithms for medical image analysis Machine Learning in Healthcare : Deep learning applications in medical imaging Computer Vision : 3D reconstruction and scene understanding Research Trends: His recent publications demonstrate a strong focus on deep learning applications in medical imaging, particularly in 3D volumetric analysis, real-time surgical guidance systems, and automated diagnostic tools. The work spans from fundamental algorithm development to clinical translation, with significant contributions in areas like neural network architectures for medical image segmentation, pose estimation for robotic surgery, and augmented reality systems for intraoperative navigation. Scientific Awards: IEEE Fellow (2022) MICCAI Society Enduring Impact Award (2021) IEEE ISMAR 10 Year Lasting Impact Award (2015) Fellow of MICCAI Society (2012) SMIT Technology Award (2010) Siemens Inventor of the Year (2001) Over 50 best paper awards at international conferences Leadership & Service: General Chair: MICCAI 2015, ISMAR 2001/2005/2014 Founding Board Member: IPCAI (2010-2021) Editorial Board Member: IEEE TMI, MedIA Steering Committee Member: IEEE ISMAR (since 2001) Board of Directors: MICCAI Society (2007-2012, 2014-2017) Laboratories & Teams: Prof. Navab leads the Laboratories for Computer Aided Medical Procedures (CAMP) at TUM, a world-renowned research group focused on developing cutting-edge technologies for computer-assisted surgery and medical interventions. The lab has produced numerous award-winning PhD students who have gone on to become leaders in the field. He also directs the biannual Medical Augmented Reality school series at Balgrist Hospital in Zurich, Switzerland, which has become a premier educational event in the field.

Alberto Salleoمشاهده پروفایل

استاد

• Organic bioelectronics
• Organic semiconductors
• Structure-property relations
• +۵ مورد دیگر

Alberto Salleo serves as Professor of Materials Science and Chair of the Department of Materials Science and Engineering at Stanford University's School of Engineering since 2019. He joined Stanford in 2005 as Assistant Professor, progressing to Associate Professor (2013) and Full Professor (2019) with a visiting professorship at École des Mines de Saint-Etienne in 2016. His research focuses on structure-property relationships in organic semiconductors , specializing in: Operando characterization techniques (X-ray/electron microscopy) Organic bioelectronics for biosensors and neuromorphic computing Development of artificial synapses and wearable diagnostic devices Electronic transport mechanisms in conjugated polymers His group's work bridges fundamental materials science with applications in healthcare and energy-efficient computing. Salleo's publication record includes multiple Clarivate Highly Cited Papers in Science , Nature Materials , and Journal of the American Chemical Society , demonstrating significant impact across organic electronics and neuromorphic engineering. His research trends show increasing focus on bioelectronic interfaces and energy-efficient computing architectures. Award highlights: Elected Fellow of Materials Research Society (2022) and European Academy of Sciences (2021) Cavaliere Ordine al Merito della Repubblica Italiana (2020) Walter J. Gores Award (Stanford's highest teaching honor, 2016) NSF Career Award and SPIE Early Career Award Salleo directs an active research group developing novel characterization methods and devices, serving as Principal Editor of MRS Communications and board member for the Materials Research Society. His leadership extends to international scientific collaboration through the Italian Scientists and Scholars in North America Foundation.

• Attosecond Spectroscopy
• Ultrafast Dynamics
• Optics and Light Physics
• +۳ مورد دیگر

Martin Schultze is a Professor at the Institute of Experimental Physics at Graz University of Technology. His research focuses on ultrafast phenomena in condensed matter and light-matter interactions, particularly in the domains of attosecond spectroscopy, spin dynamics, and metaoptics. He also explores applications in optics, nanophotonics, and marine litter monitoring. His work bridges fundamental physics with advanced experimental techniques, including the development of attosecond-scale measurement tools. Education details are not explicitly provided in the text. However, his professional qualifications include Dipl.-Phys. (Diploma in Physics) and Dr.rer.nat.habil. (Habilitation in Natural Sciences). Research interests span ultrafast spin dynamics, attosecond current measurements, and the control of light fields for nanoscale applications. Notable areas include the study of conduction band dynamics in semiconductors, surface plasmon polaritons, and the design of advanced optical materials like metalenses. He also contributed to collaborative projects in marine litter monitoring, though this appears to be a secondary focus. His publications emphasize methodological advancements in ultrafast laser technology and spectroscopy, with contributions to understanding light-induced magnetic effects and spintronics applications. No scientific awards are explicitly mentioned in the provided data. Consultation hours are held weekly at TU Graz, and his lab is situated at the Institute of Experimental Physics in Graz. He actively engages in teaching experimental physics, specializing in optics and light physics fundamentals.