The Climate Risk Lab is accepting applications for its Data Science and Machine Learning Fellowship, a full-time research opportunity for recent graduates passionate about using artificial intelligence and data science to tackle climate change. Based in Cape Town, South Africa, the fellowship offers early-career researchers the chance to work with interdisciplinary experts on cutting-edge projects involving climate, environmental, health, and socioeconomic data. Applications are reviewed on a rolling basis, with first candidate reviews beginning in October 2026 and a preferred start date of February 2027. About the Climate Risk Lab The Climate Risk Lab develops breakthrough science, tools, and policy solutions that help protect people and nature from the impacts of climate change. Its research combines climate science, machine learning, epidemiology, environmental data, and public policy to better understand climate risks and identify evidence-based solutions that improve human health and wellbeing. Current projects include predictive climate-impact modelling, AI-assisted evidence synthesis, and the analysis of large-scale spatial and temporal datasets. The fellowship is designed to strengthen both the laboratory’s research capacity and the technical skills of successful candidates through mentorship and hands-on research. Fellowship Benefits This is a full-time, one-year fellowship with the possibility of a one-year renewal based on performance. Outstanding fellows may also have opportunities to progress into a PhD scholarship or postdoctoral research position within the lab. Successful candidates will receive: Competitive remuneration based on academic and market rates One-to-one mentorship from senior data scientists and postdoctoral researchers Opportunities to lead an independent research project Participation in international collaborations, working groups, and conferences Experience publishing peer-reviewed research, datasets, or analytical toolkits The fellowship is based in Cape Town, with occasional international travel where required. Who Should Apply? The fellowship targets recent PhD, MSc, or first-class Honours graduates from quantitative disciplines who want to apply data science to real-world climate challenges. Eligible academic backgrounds include: Data Science Machine Learning Computer Science Mathematics Statistics Econometrics Engineering Physics Related quantitative fields The Climate Risk Lab particularly encourages applications from historically underrepresented groups, including female-identifying people of colour. Key Responsibilities Fellows will provide data science support across interdisciplinary climate research projects while developing their own research agenda. Responsibilities include: Analysing large climate, environmental, health, and socioeconomic datasets Developing AI tools to improve research workflows Cleaning, processing, and optimising large datasets Building reproducible analytical pipelines using Git and version control Maintaining efficient code for large spatial-temporal datasets Contributing to peer-reviewed publications, datasets, and research tools Collaborating with international research partners Minimum Requirements Applicants should demonstrate strong quantitative and programming skills. Essential criteria include: MSc or first-class Honours degree (completed or final year) Background in data science or a related quantitative discipline Proficiency in Python or R Strong knowledge of statistics and data science principles Excellent written and verbal communication skills Desirable Technical Skills While not mandatory, candidates with experience in the following areas will be highly competitive: Python and R libraries including pandas, numpy, xarray, terra, tidyverse, zarr, netCDF4, arrow, and duckdb Linux, Bash, and high-performance computing (HPC) GitHub and collaborative software development Agentic AI tools and machine learning workflows Large spatial-temporal environmental datasets Required Application Documents Applicants must combine all required documents into one PDF before submission. The application should include: Curriculum Vitae (CV), including South African work or study permit status Two professional referees with names, positions, and email addresses Cover letter explaining suitability for the fellowship Academic transcript Optional GitHub, Jupyter Notebook, or coding portfolio links Optional personal website or LinkedIn profile How to Strengthen Your Application Strong applicants will demonstrate both technical excellence and a clear interest in climate research. A compelling cover letter should explain how previous experience in machine learning, statistics, or computational research aligns with the Climate Risk Lab’s mission. Including a GitHub portfolio or well-documented coding projects can also strengthen an application by showcasing reproducible research practices and programming ability. Application Timeline The fellowship operates on a rolling recruitment basis, meaning applications are considered as they are received. Key DateDetailsApplications OpenRollingFirst Reviews BeginOctober 2026Preferred Start DateFebruary 2027InterviewsTechnical interview for shortlisted candidatesClosing DateUntil the position is filled Official Application Applications must be submitted through the official Google Form provided by the Climate Risk Lab. Candidates with questions about the fellowship may contact Puja Pande at puja.pande@uct.ac.za. This fellowship provides an exceptional opportunity for aspiring data scientists and AI researchers to contribute to impactful climate research while building advanced technical and academic expertise within one of Africa’s leading interdisciplinary climate research teams. VIEW THE FINAL CALL HERE For more opportunities such as these please follow us on Facebook, Instagram, Twitter, LinkedIn and WPChannel Disclaimer: Global South Opportunities (GSO) does not take ownership or credit for the opportunities shared on this platform. All opportunities are sourced from official announcements and external sources for informational purposes only. Applicants are encouraged to verify all details, deadlines, and eligibility requirements from the official source before applying. Opportunities may be updated or changed by the providing organisations.