Current Calls

Here you will find an overview of current offers for bachelor and master theses in the interdisciplinary field of synthetic biology.

  • RNA plays an important role in both the transcription and translation processes. Unlike bacteria, in eukaryotes, RNA will be transported, localized, and locally translated. Therefore, it is meaningful to detect the localization and quantify the RNA. RNA-FISH is one of the most popular methods to quantify and localize RNA in fixed cells. For this reason, we would like to establish the RNA detection in Saccharomyces cerevisiae using fluorescence in situ hybridization (FISH).

    Supervisor: Prof. Dr. Heinz Koeppl

    Announcement as PDF

  • Relaxed continuous time Markov chains

    Masterthesis, Bachelorthesis, undergraduate assistent

    2021/08/29

    Deep generative models such as the variational autoencoder have led to breakthroughs in generating synthetic data from complicated distributions, e.g. natural images. The key ingredient of this success story is a new way of parameter learning via sampling-based variational inference.

    Supervisor: Christian Wildner

    Announcement as PDF

  • The laboratory of Viktor Stein takes a protein-centric approach to synthetic biology as we devise systematic approaches to engineer artificial sensory, signalling and transport functions focussing on the construction of protein switches, optical sensors, protein nanopores and membrane transporters. Strategically, we address fundamental questions exploring the design principles of artificially engineered proteins and develop them towards distinct biotechnological applications. Our work also entails a strong focus on the development and application of enabling technologies. This includes new DNA assembly methods (e.g. iFLinkC2) to assemble protein switches and sensors via combinatorial linker libraries and genetic screening systems (e.g. FuN Screen3) to study and engineer transport processes across microbial membranes. These high-throughput approaches are complemented by high-resolution analytical methods (e.g. electrophysiological measurements in lipid bilayers3 and live cell fluorescence microscopy in microfluidics3) to better understand the molecular features that underlie the function of artificially engineered proteins.

    Supervisor: Prof. Dr. Viktor Stein

    Announcement as PDF

  • 2021/08/29

    The aim of the research project is to use deep neural networks to identify patterns in the gene sequences of homologous proteins that are responsible for successful expression. Based on these patterns, a prediction of the producibility of heterologous proteins will be made using their DNA sequence and, if necessary, verified with available experimental data.

    Supervisor: Prof. Dr. Heinz Koeppl

    Announcement as PDF