12604 - Scientific Software Developer - Climate AI

at  CMCC Foundation

WHAT WE ARE LOOKING FOR

The Division of Regional Models and geo-Hydrological Impacts (REMHI) at the Institute for Climate Resilience (ICR) is seeking a new Scientific Software Developer to support the design, development, and deployment of AI-based applications for climate science. This role focuses on data-driven climate analysis at very high spatial and temporal resolution and the nowcasting of severe events using satellite data.

ABOUT US

CMCC Foundation is a cross-cutting scientific research centre on climate change and its interactions with the environment, society, the world of business, and policymakers. Our work aims to stimulate sustainable growth, protect the environment, and develop strategies for the adaptation and mitigation of climate change. CMCC’s core objective is to conduct cutting-edge science, to train the next generation of scientists at both national and international levels, and to be a beacon for climate modelling.
CMCC pursues fundamental and applied science with utmost scientific integrity, prioritising data-driven science and providing data, information, and research results that can support informed public debate and decision-making processes. To achieve climate research objectives at the highest international standards, we invest in training all our talents, and strive to create a workplace where everyone can excel.
At CMCC you will find a strong, professional environment. Join an inclusive community that values diversity, where every voice is heard and respected. Help foster a culture of innovation and societal change, where individuals from all backgrounds can thrive and succeed.

REQUIREMENTS

• Master’s degree in Computer Science, Computer Engineering, or equivalent field;
• Experience in Data Science, Machine Learning, and Deep Learning;
• Proficiency in Python programming for scientific applications;
• Familiarity with common tools and libraries like numpy, pandas, xarray, scikit-learn, and matplotlib;
• Experience with PyTorch is preferred; knowledge of TensorFlow or Keras is also acceptable;
• Basic understanding of HPC and distributed computing for training large models;
• Experience with data preprocessing, feature engineering, model evaluation, and tuning ML algorithms;
• Ability to work independently and as part of a team;
• Good knowledge of the English language.

ADDITIONAL SKILLS:

• Experience in atmospheric science applications;
• Strong problem-solving attitude;
• High motivation to learn and collaborate;
• Experience in managing NetCDF e CDO;
• Contributing to scientific paper production.

ROLE

The Scientific Software Developers specializing in Machine Learning report directly to the REMHI director and are tasked with supporting the existing REMHI team in the end-to-end design, development, and deployment of AI-based applications. Specifically, this role focuses on:

• Downscaling Climate Data: Developing and refining algorithms to downscale climate data to very high spatial and temporal resolutions. This involves creating models that can accurately translate coarse-resolution climate data into detailed, high-resolution datasets, enabling more precise climate predictions and analyses.
• Nowcasting Severe Events: Designing and implementing AI-driven systems for the real-time prediction (nowcasting) of severe weather events. This includes leveraging satellite data and advanced machine learning techniques to monitor, detect, and forecast extreme weather events with high accuracy and timeliness.

The role requires proficiency in machine learning, data analysis, and climate science, as well as experience with satellite data processing and high-performance computing.

RESPONSIBILITIES

The Scientific Software Developers will support the REMHI Division, with:

• Algorithms development:
• Develop and refine AI algorithms to downscale climate data to very high spatial and temporal resolutions;
• System Design and Implementation:
• Design and implement AI-driven systems for real-time prediction (nowcasting) of severe weather events;
• Leverage satellite data and advanced machine learning techniques for monitoring, detecting, and forecasting extreme weather events;
• Data Processing:
• Process and analyze satellite data to support their use in the context of climate predictions and definition of nowcasting systems;
• Manage and manipulate large datasets for machine learning and climate analysis purposes;
• High-Performance Computing (HPC):
• Optimize software and AI algorithms to efficiently run on HPC infrastructure.