Job Description: Exciting and interesting technical challenges await when you join a world-class research team dedicated to reducing the impact of weather and climate risks. FM is a market leader in commercial and industrial property insurance and loss prevention, providing more than one-third of FORTUNE 1000 companies with engineering/science-based risk management and property insurance solutions. FM helps clients maintain continuity in their business operations by drawing upon state-of-the-art engineering and research.Climate risk is an identified area with increasing challenges in the next decades. To provide the best-in-class actionable science-based loss preparedness and prevention solutions to businesses, FM is making vast investments into climate research over the coming years, including the new Science and Technology Center EMEA in Luxembourg, from which the global Climate Risk and Resilience Research is being headquartered from and the expansion of the Science and Technology Center APAC in Singapore. Qualifications: Qualified candidates must have:· PhD in Computer Science, Geography, Environmental Engineering, or a related field.· Fluent in Python and at least one other language (e.g., R, Fortran, Matlab).· Strong experience in geospatial modelling, particularly in natural hazard contexts.· Proven ability to implement and operationalize deep learning models.· Hands-on experience with deep learning frameworks (e.g. PyTorch, TensorFlow, JAX), with demonstrated ability to apply architecture like LSTM, U-Net, and Vision Transformers· Proficiency with open-source geospatial tools (e.g., GDAL, rasterio, shapely, PostGIS) and GIS platforms· Skilled in handling large datasets and automating data pipelines via scripting and APIs.· Excellent communication skills and ability to work in high-performance, research-driven teams. Desired Skills and Competency Areas· Experience with HPC environments, GPU parallelization, and cloud platforms (AWS, Azure).· Familiarity with Databricks for scalable data engineering and machine learning workflows, including Spark-based distributed processing and ML model lifecycle management.· Familiarity with DevOps practices for scalable model deployment.· Ability to communicate complex technical insights to non-specialist stakeholders.· Experience in remote sensing techniques and data analysis, including satellite imagery interpretation, photogrammetry, and LiDAR processing.· Good understanding of one or more physics-based systems and processes, particularly those related to flood dynamics, hydrometeorological extremes, tropical cyclones, and synoptic-scale weather systems
