Context And Mission

The successful candidate will join the Computational Earth Sciences group to contribute to the design and development of an AI-based atmospheric emulator. This emulator will be used to simulate climate trajectories generated by very high-resolution, state-of-the-art climate models in the framework of Digital Twins of the Earth. The candidate will focus on creating a reliable and efficient emulator that can replicate the behaviour of complex climate models, enabling much faster simulations, saving energy, opening up new simulation protocols and interaction possibilities, and supporting climate science research.

The funding for these actions/fellowships and contracts comes from the European Union Recovery and Resilience Facility - Next Generation, within the framework of the General Invitation by the public business entity Red.es to participate in the talent attraction and retention programs within Investment 4 of Component 19 of the Recovery, Transformation, and Resilience Plan.
For more information, please check: https://www.bsc.es/join-us/excellence-career-opportunities/ai4s

"La financiación de estas actuaciones/becas y contratos, procede del Mecanismo de Recuperación y Resiliencia de la Unión Europea-Next Generation, en el marco de la Invitación General de la entidad pública empresarial Red.es para participar en los programas de atracción y retención del talento dentro de la Inversión 4 del Componente 19 del Plan de Recuperación, Transformación y Resiliencia.
Para más información: https://www.bsc.es/join-us/excellence-career-opportunities/ai4s "

Key Duties

Design and develop an AI-based atmospheric emulator based on state-of-the-art climate models to simulate climate trajectories.
Implement, test, and validate machine learning models that reproduce the output of high-resolution climate simulations.
Collaborate with climate scientists to ensure the accuracy and relevance of the emulator’s predictions.
Optimize the emulator for performance and scalability on high-performance computing (HPC) systems.
Document the development process and provide support for users of the emulator.

Requirements

Education
Bachelor’s degree in Computer Science, Artificial Intelligence, or a related discipline.
A Master’s degree in a relevant field will be highly valued.
Essential Knowledge and Professional Experience
Strong programming skills in Python and experience with AI/ML frameworks such as TensorFlow or PyTorch.
Familiarity with state-of-the-art deep learning architectures and techniques.
Experience in developing and deploying machine learning models.
Experience in processing and managing large quantities of data for machine learning training pipelines.
Familiarity with UNIX/Linux environments and scripting languages.
Additional Knowledge and Professional Experience
Fluency in English is essential. Proficiency in Spanish and other European languages would be advantageous.
Knowledge of climate science or experience working with climate models is highly valued.
Experience with high-performance computing (HPC) and optimizing models for large-scale simulations.
Experience with version control systems like Git.
Competences
Ability to work independently and within a team to achieve project goals.
Strong problem-solving skills and attention to detail.
Excellent communication skills to collaborate effectively with interdisciplinary teams.