Junior Visiting Researcher

This role seeks a Junior Visiting Researcher for a global project on adaptive intelligence for nested energy systems. Key responsibilities include developing and training AI/ML models for building energy demand modeling, utilizing geospatial and climate data, and designing scalable cloud-native data pipelines. A strong background in machine learning, data science, Python, cloud platforms, and experience with imperfect real-world datasets are required.

• Design and implement cloud native data pipelines and scalable infrastructure for ingesting, processing, and serving large geospatial, building energy and climate datasets
• Train, fine-tune, and evaluate machine learning models for building energy demand projection, renewable energy supply forecasting, and urban energy system optimization
• Work with large geospatial, building energy and raster datasets (climate grids, building footprints, urban morphology) using Python geospatial libraries and climate data (MERRA-2, ERA5, CMIP)
• Develop clean, well-documented Python code with Git/GitHub and set up reproducible workflows (environments, testing, documentation)
• Contribute to documentation, project reporting and peer-reviewed publications
• Design and execute experiments to evaluate model performance, uncertainty, sensitivity, and transferability across geographies and scales
• Engage in code review and pilot project work across institutions

• Strong foundation in machine learning: understanding of supervised/unsupervised methods, model evaluation, feature engineering, and common pitfalls
• Artificial Intelligence related experience
• Solid proficiency in Python for data science and ML
• Experience training and fine-tuning ML models (in academic or professional projects)
• Experience with cloud platforms (Azure, AWS, Google Cloud) and basic data engineering (ETL pipelines, databases)
• Experience with containerization (Docker) or workflow orchestration
• Experience with heterogeneous real-world datasets, ability to assess data quality, handle missing values, and work iteratively with imperfect information
• Strong written and spoken communication skills in English
• Masters degree (or equivalent) in a quantitative field (data science, computer science, environmental science, physics, engineering, or related) · Demonstrated ability to work independently and manage tasks with self-direction
• willingness to learn new tools and methodologies