Cancer mutanome vaccines targeting neoepitopes derived from somatic mutations have ideal properties to become an essential part of modern multimodal cancer therapy. Our goal is to fully realize this personalized cancer immunotherapy concept by addressing the key genomic and immunological challenges for successful application of this approach in patients with any type of cancer.

The ESA Gaia satellite mission delivers ultra-high precision data for astronomy and fundamental physics. Converting the raw data to a usable form is one of the largest computational challenges ever solved in observational astronomy. The local astronomy group at TUD is responsible for the core computations, calibration and relativistic modeling of the data and part of the European Gaia data consortium. The usage of the local HPC system is absolutely essential for this work.

In the joint BMWi Lufo VI project DARWIN, the Center for Information Services and High Performance Computing (ZIH) and the Chair of Turbomachinery and Aero Engines (TFA) at the TU Dresden are working in cooperation with Rolls Royce Germany on the further development, application and validation of innovative digital simulation and design methods to improve the interdisciplinary understanding of engine systems. Work includes improving load balancing of highly parallelized coupled simulation codes, measuring surface roughness and wear effects and feeding them back into simulation models, as well as applying machine learning (ML) methods to predict flow fields.

The design of new functional proteins can tackle many of the problems humankind is facing today but so far has proven very challenging1. Analogies between protein sequences and human languages have been long noted and a summary of their most prominent similarities is described. Given the tremendous success of Natural Language Processing (NLP) methods in recent years, its application to protein research opens a fresh perspective, shifting from the current energy-function centered paradigm to an unsupervised learning approach based entirely on sequences. To explore this opportunity further we have pre-trained a generative language model on the entire protein sequence space. We find that our language model, ProtGPT2, effectively speaks the

Strong painkillers (such as opioids) are essential to medicine. However, they are mostly addictive and have potentially deadly side effects. Simulation and machine learning techniques help in the search for tailor-made active substances that do not have these side effects. The interaction of the various algorithms raises questions about which computer architecture can support the calculations most effectively.