Johannes Kepler University Linz

Institute of Bioinformatics / Institut for Machine Learning

The Institute of Bioinformatics conducts internationally renowned research and provides profound education in bioinformatics. Its research focuses on development and application of machine learning and statistical methods in biology and medicine.

The institute for machine learning has a researc hfocus on:

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Long Short-Term Memory
  • Reinforcement Learning
  • Vision
  • Representational Learning
  • Natural Language Processing (NLP)
  • Bioinformatics (Genetics, Genomics)



The Supercomputer MACH-2

MACH-2 is a massively parallel shared memory supercomputer. The system was installed in October 2017 and is fully operational since January 2018. MACH-2 is named after the Austrian physicist and philosopher Ernst Mach. MACH-2 is a machine of type SGI UV 3000 of the former company Silicon Graphics International (SGI), now Hewlett Packard Enterprise (HPE). It belongs to the class of cache coherent Non-Uniform Memory Access (ccNUMA) architectures which are massively parallel supercomputers that implement a global shared memory model on top of scalable distributed hardware. MACH-2 is housed in three racks of this kind and has the following characteristics:

Operator: Scientific Computing Administration

Hardware: 1728 cores, 20 TB global shared memory, 260 TB storage

Software: a few boinformatics specific programs

Homepage: MACH-2



University of Applied Sciences Hagenberg

Research group Bioinformatics

In bioinformatics, the Upper Austria University of Applied Sciences supports specialists from the life sciences, such as doctors, biologists and geneticists, with intelligent software systems for the analysis of molecular biological data and the simulation of biological processes. Known algorithmic approaches of bioinformatics are algorithms for genomics, for sequence analysis, for proteomics (researching the proteome, ie the proteins found in a living being), for structural bioinformatics, and last but not least also for the use of machine learning methods in analysis biological data.