Context And Mission
Within the Computational Earth Sciences group, the Models and Workflows (MWT) team studies and develops essential frameworks and tools for efficiently running modeling experiments on HPC infrastructures. MWT is a growing team of approximately 30 members, closely collaborating with the Performance, Data, and AI teams, as well as the other scientific groups within the Department.
The successful candidate will contribute to the deployment and development of state-of-the-art external and in-house workflow solutions supporting the complete lifecycle of AI-based scientific workflows, from data preparation and model training to validation, deployment, and reproducible execution. The work will be carried out within European research projects developing next-generation AI-enabled digital twins for climate applications, integrating HPC systems, AI infrastructures, and large-scale data services.
The position offers the opportunity to collaborate with leading European research institutions in the areas of AI, climate science, workflow technologies, and research software engineering.
Learn more on our website: https://bsc.es/research-development/research-areas/computational-earth-sciences/models-and-workflows-team
Key Duties
Develop, maintain, and deploy workflow solutions for AI-driven scientific applications. Contribute to the development of reproducible and traceable computational workflows following FAIR principles and research software engineering best practices. Collaborate with AI researchers, climate scientists, and software engineers to integrate new AI models into operational workflows. Contribute to software testing, continuous integration, documentation, and dissemination of developed technologies. Participate in European collaborative research projects and interact with international partners.
Requirements
Education BSc in Computer Science, Computational Science, Data Science, Artificial Intelligence, Software Engineering, or a related discipline. Having a Master’s degree will be valued. Essential Knowledge and Professional Experience Experience in scientific software development using Python. Experience deploying and developing workflow solutions for scientific or AI applications. Experience with workflow management systems (e.g. Autosubmit, Airflow, or similar). Experience with AI model lifecycles Experience working with Linux environments and HPC systems. Experience using version control systems (Git) and collaborative software development practices. Experience with container technologies such as Docker or Singularity. Knowledge of software engineering best practices, including testing, documentation, and continuous integration. Additional Knowledge and Professional Experience Troubleshooting and debugging skills. Good written and spoken English. Understanding of HPC computer architecture issues, including CPU, accelerators, memory, interconnect, parallel I/O, and computational performance in general. Knowledge of HPC job schedulers (Slurm, PBS or similar). Knowledge of climate or Earth system modelling workflows. Experience participating in European or international research projects. Contributions to open-source software projects. Competences Earth system models are sophisticated tools and High-Performance Computers are complex systems. The candidate needs to have excellent problem-solving skills and a proactive attitude to address new challenges and perfect the current solutions so they gain reliability and efficiency. This is a specialized position so the successful candidate is expected to have a demonstrated learning capacity and the motivation to maintain a learning progression during the contract. The candidate will work within international collaborative projects, so it is mandatory to be able to fulfill schedules and coordinate with members from other institutions, as well as disseminate the advances in international workshops.
