Location: Remote, Africa

Full Time

Role Overview

You will build, train, and optimize physics-informed ML models for climate and environmental prediction. Work hands-on with PINNs, operator learning, and HPC/GPU workflows to push the boundaries of AI for scientific computing.

Key Responsibilities

• Develop physics-informed ML models and neural operators
• Integrate scientific PDEs, constraints, and domain knowledge into models
• Train large-scale scientific ML models on HPC/GPU clusters
• Collaborate with climate scientists and geospatial engineers
• Optimize models for speed, accuracy, and stability
• Support deployment of scientific models into production pipelines

Requirements

• 3-5 years experience in scientific ML, PINNs, or operator learning
• Experience with PyTorch, JAX, or TensorFlow for custom model development
• Understanding of PDEs, numerical methods, and physical modeling
• Experience training ML models on multi-GPU or distributed systems
• Ability to collaborate in scientific workflows and interdisciplinary teams
• Comfortable communicating in English, both written and verbal

Preferred

• Experience with climate models, environmental physics, or computational science