The Institute of Visual Computing & Human-Centered Technology at Vienna University of Technology (TU Wien) is seeking a highly motivated PhD candidate to join the IVILPC research project. The three-year project aims to overcome the challenges of efficiently rendering large point cloud data sets for a range of practical, real-world use cases.
This image shows a Proliferating cell nuclear antigen (PCNA) molecule sliding along the DNA in a rotation-coupled translation manner. It is part of an animation explaining the process of DNA replication. It was rendered by Marion/Vj real-time molecular animation framework.
In WWTF’s 2022 Information and Communication Technology call, TWO out of nine winning projects (from TU Wien) were submitted by researchers from our computer graphics research unit.
The call was open to researchers from universities and non-university institutions in Vienna and Lower Austria with a funding range per project of 400,000 EUR – 880,000 EUR. The competition was extremely high: From the 67 submitted short proposals, 26 have been invited to the next stage to submit a full proposal. The funding volume for the nine winning projects amounts to over 6,000,000 EUR.
Der gemeinsame Fachbereich Informatik in den Lebenswissenschaften der Gesellschaft für Informatik und der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie vergibt den ILW Förderpreis 2022 für die beste Masterarbeit (Studienabschlussarbeit) aus dem Kontext: Informatik in den Lebenswissenschaften
Dazu gehören insbesondere Arbeiten aus den Bereichen Bioinformatik, Umweltinformatik und Medizinische Informatik.
Der Preis ist mit 1000 € dotiert.
We are happy to announce two recent awards at Eurographics Workshop on Visual Computing for Biology and Medicine (EG VCBM) 2022:
1) A Honorable Full Paper Mention for the work "MuSIC: Multi-Sequential Interactive Co-Registration for Cancer Imaging Data based on Segmentation Masks" by Tanja Eichner, Eric Mörth, Kari Wagner-Larsen, Njål Lura, Ingfrid Haldorsen, Eduard Gröller, Stefan Bruckner, Noeska Smit
Hervorragende Ergebnisse durch institutsübergreifende Kooperationen
Theresa Neubauer entwickelte eine neue Bild-Segmentierungsmethode von Tumoren, die maschinelles Lernen (Künstliche Intelligenz) verwendet, um komplexe Bildmerkmale und Zusammenhänge zwischen den Modalitäten zu lernen und somit den Tumor effizienter und präziser segmentieren zu können.
