RESEARCHER IN MACHINE LEARNING AND MLOPS (2-YEAR FULLTIME FIXED-TERM CONTRACT) (AU.08.4)

Brief Description: Requirements A Master’s degree or PhD in Computer Science, Machine Learning, Data Science, Software Engineering, Applied Mathematics, or a related field. Strong programming skills in Python and experience with modern machine learning frameworks such as PyTorch, TensorFlow, or JAX. Experience building and maintaining machine learning pipelines and data workflows. Experience working with large-scale scientific or geospatial datasets. Experience with high-performance computing environments, GPU clusters, or cloud platforms (e.g., GCP, AWS, or similar). Experience using version control systems (e.g., Git) and collaborative software development practices. Ability to work effectively in interdisciplinary research teams. Strong problem-solving skills and attention to reproducibility and reliability in scientific computing workflows. Experience in one or more of the following areas will be advantageous: MLOps and machine learning infrastructure Distributed training of deep learning models Geospatial data processing and climate datasets Containerization technologies such as Docker and Kubernetes Weather or climate modelling systems Forecast verification or climate data analytics Key Responsibilities Develop and maintain machine learning infrastructure and pipelines supporting the FineCast research programme. Build scalable workflows for training, evaluating, and deploying AI-based weather and climate forecasting including generation, compression and  archival of  hindcasts from frontier models. Manage and process large meteorological and climate datasets, including satellite data, reanalysis products, and observational station data. Support the training and fine-tuning of global AI weather prediction models using regional datasets. Implement systems for experiment tracking, reproducibility, and model versioning in machine learning research. Develop tools and infrastructure supporting forecast verification and model benchmarking. Support the integration of weather forecasting outputs with climate risk modelling and analytics workflows developed within the School’s research programme. Contribute to the development of open-source software and research tools produced by the project. Work closely with researchers and postgraduate students to translate research ideas into scalable and reliable machine learning systems