Scientific IT Services (SIS) enable modern research through the provision of first-class scientific computing services. From data management plans to individual consulting and data analysis, we dedicate our work directly to researchers, and we can support you both in solving problems as well as in planning future projects.

High Performance Computing Central CPU Euler cluster • CPU&GPU Leonhard Open cluster designed for big-data and AI applications • Secure Leonhard Med cluster dedicated for biomedical applications • Updates and tutorials in SciComp Wiki • Around 80000 processor cores for scientific calculations Read more..

Software Development Individual software development • Custodianship of existing code • Scientific software • User interfaces for data collections • Custom workflow solutions • Interactive data visualisation • Lab automation Read more..

Scientific Computing & Data Co-Analysis Data analysis workflows • Co-analysis of your data • Reproducible research • Code optimization & parallelization (scale up & out) • Efficient use of your computing resources (e.g. GPU, HPC, cloud) Read more..

Scientific Visualization 2D & 3D animations • Web-map services • Distributed/parallel visualizations • Google earth animations • Swiss topo • OmniGlobe animations @ Focus Terra Museum • Visualization using Python training Read more..

Data Science & Machine Learning Deep learning • Natural language processing • Image and pattern recognition • Computer vision • Hands-on machine learning training • Big data • Classification & regression Read more..

Research Data Management Research Data Hub • Department Data Hub • Research Data Node • openRDM.swiss • openBIS platform • Data Management Plan Read more..

Confidential Research Data Secure scientific Data & IT infrastructure • Leonhard Med platform • Biomedical research • Personalized health data services • HPC and cloud compute • Large volume data analysis and management • Interoperability • SPHN, PHRT, SDSC • BioMedIT node Zurich Read more..

Consulting & Training

We hold regular Code & Data Clinic sessions where researchers can stop by to get help with their own source code.

To learn new skills or improve existing skills, researchers can also participate in our courses and workshops on various problem of scientific computing that we developed based on the needs of our users.

The next Code & Data Clinics and upcomping courses will be announced in our news section.

Courses	Duration
openBIS & ETH RDH Training	3.5 hours
Analyzing data stored in openBIS with Jupyter and Matlab	1.5 hours
Research Data Management Workshop Series	5 days
High Performance Computing for Genomic Applications	1.5 days
Getting started with scientific clusters	4 hours
Parallel programming with MPI/OpenMP	4 days
Introduction to Python	1 day
Introduction to programming with Python	8-10 lectures
Introduction to Machine Learning with Python	3 days
Scientific Visualization using Python	2 days
Workshop on best practices in Programming (git, unit testing, clean code)	2 days

Read more..