Research Data Expert

In this role, your responsibilities and work will primarily consist of training and consulting ETH researchers in the areas of research data management (RDM) and reproducible research. This includes, for instance:

• developing and presenting training materials that enable scientists to adopt FAIR data principles in their daily research;
• transforming training materials into innovative new formats such as online modules in learning management systems;
• supporting researchers in the development of discipline-specific RDM training materials;
• helping researchers in adopting FAIR principles by developing and supporting suitable computational / IT solutions;
• translating researchers’ requirements into practical RDM solutions through development and integration of modern RDM software systems (e.g., plugins, web apps, workflows).

You will work closely in a team of SIS experts, comprising RDM experts, research software engineers, data scientists, etc.

This position requires

• PhD degree in data / computational science or related field such as physics, bioinformatics / computational biology, computer science, etc.;
• familiarity with the FAIR data principles and the research data life cycle;
• experience in teaching and a passion for presenting abstract concepts in an engaging way;
• working knowledge with research data management according to the FAIR principles and corresponding software systems (e.g., openBIS);
• hands-on experience with best practices for code management and reproducible research (e.g., Git, Conda, Docker) and with working in the Unix/Linux environment;
• strong communication and coordination skills to engage with researchers in different disciplines regarding their RDM challenges.

Advantageous are experience and/or working knowledge with the following topics:

• Python and its scientific libraries (e.g., Numpy, Pandas);
• creating online course materials using learning management systems (e.g., openEdx) and familiarity with the concept of Open Educational Resources (OER);
• applying best practices in scientific programming (e.g., automated testing and version control), container technologies and workflow management systems.

In addition to the above analytical and technical abilities, this position demands excellent communication skills in English (German is an asset) and didactic skills, a commitment to continuous learning as well as the interest in, curiosity and ability to effectively collaborate with domain scientists and IT specialists alike.