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Research Associate

Research Associate

AfriClimate AIWorkFromHome, KwaZulu-Natal, South Africa
30+ days ago
Job description

Overview

The Research Associate will support AfriClimate AI’s applied research portfolio by contributing to the development, fine-tuning, and validation of AI-driven climate and weather modelling tools. Working closely with the research team, the successful candidate will play a hands-on role in data preparation, model development and evaluation, ensuring that research outputs are robust, open and relevant for African contexts.

Research & Development

  • Assist in designing, implementing, and testing AI-based climate and weather forecasting methodologies.
  • Preprocess and manage large geospatial datasets (e.g., observational data, satellite products, reanalysis datasets).
  • Support the development of benchmarking frameworks for model evaluation, including skill scores, bias correction and uncertainty quantification.
  • Contribute to the setup and maintenance of MLOps pipelines for training, deployment and monitoring of AI models.
  • Collaborate with team members on documentation, and reproducibility of workflows.

Collaboration & Knowledge Sharing

  • Work with meteorological agencies, universities and international partners on joint research tasks.
  • Contribute to open-source datasets, code repositories and technical documentation.
  • Support the preparation of research outputs, including peer-reviewed articles, technical reports, and policy briefs.
  • Present results in internal and external meetings (workshops and conferences).
  • Essential Qualifications & Experience

  • Master’s degree in Climate Science, Meteorology, Machine Learning or a related field.
  • Proficiency in Python.
  • Experience working with geospatial and gridded datasets (e.g., ERA5, CHIRPS, CMIP, satellite-based products).
  • Knowledge of machine learning methods applied to climate or weather problems.
  • Familiarity with standard model evaluation metrics and statistical analysis.
  • Ability to work independently as well as collaboratively in distributed teams.
  • Ability to communication technical work clearly.
  • Desirable Skills & Experience

  • Previous research experience on African climate datasets and / or data-sparse contexts.
  • Hands-on experience with deep learning frameworks (e.g., PyTorch, TensorFlow).
  • Exposure to MLOps tools (e.g., MLflow, Docker, Kubernetes, Airflow).
  • Familiarity with cloud computing (AWS, GCP, Azure) or HPC workflows.
  • Knowledge of numerical weather prediction (NWP) systems, downscaling, or data assimilation.
  • Experience contributing to open-source projects or collaborative codebases.
  • AfriClimate AI

    AfriClimate AI is a grassroots research organisation advancing climate resilience in Africa through open, community-driven AI research. We focus on developing region-specific datasets, tools, and methodologies to bridge the gap between global models and local needs, supporting equitable and actionable climate solutions across the continent.

  • Mission-driven impact : Contribute to climate resilience and equity across Africa through open, locally grounded research.
  • Flexible, remote-first work : Collaborate with an international network while working from anywhere in Africa.
  • Open science ethos : Work in a fully open-source, community-driven environment that values transparency, reproducibility, and shared ownership.
  • Professional growth : Access mentorship, attend leading conferences, and shape the future of climate AI research in the Global South.
  • Collaborative culture : Join a multidisciplinary, values-aligned team working at the intersection of science, technology, and social impact.
  • Travel opportunities : Participate in key events, workshops, and field collaborations across Africa and beyond.
  • Competitive compensation : Receive a salary package that reflects your expertise, with flexibility for different levels of experience and location.
  • #J-18808-Ljbffr

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    Research Associate • WorkFromHome, KwaZulu-Natal, South Africa

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