Full-Stack Software Engineer for Reproducible Science

CERN - European Organization for Nuclear Research; Genf; Hybrid; CHF 59.000 - 65.000; Vollzeit

Benefits dieser Stelle

Monthly stipend CHF 5266–5793

Zusammenfassung

CERN invites software developers to contribute to the open-source REANA platform for reproducible computational data analyses. You will help build federated workflows across Europe as part of the EOSC Federation and Mesh project, collaborating with partners and researchers to enable large-scale open data analysis.

Role involves deploying and operating services on Kubernetes, and supporting cross-node computational data analyses with strong emphasis on open-source collaboration and

Qualifikationen

• Experience in full-stack software development and frontend/backend engineering.
• Experience with container technologies and deployment, monitoring, and security.
• Some familiarity with computational research data analyses is a plus.
• Experience in EC co-funded projects is a plus.
• Strong problem-solving and communication skills; interest in open-source collaboration.

Aufgaben

• Collaborate with EOSC project partners on cross-node federated analysis workflow infra.
• Advance CERN Node in the EOSC Federation to enable access to open data from experiments.
• Contribute to service deployment, operations and monitoring on Kubernetes cloud.
• Work with researchers to support cross-node computational data analysis examples.

Kenntnisse

Full-stack development

Frontend/backend

React

Container technologies

Communication

Ausbildung

Bachelor’s or Master’s degree

Tools

Docker

Kubernetes

Jobbeschreibung

CERN invites software developers to contribute to the open-source REANA platform for reproducible computational data analyses. You will help build federated workflows across Europe as part of the EOSC Federation and Mesh project, collaborating with partners and researchers to enable large-scale open data analysis.

Role involves deploying and operating services on Kubernetes, and supporting cross-node computational data analyses with strong emphasis on open-source collaboration and