Data Science & Machine Learning Fellowship 2027

Contexte

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.

Conditions et avantages

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.

Profil recherché

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.

Missions et responsabilités

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

Dossier de candidature

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.

Comment postuler

The fellowship operates on a rolling recruitment basis, meaning applications are considered as they are received.

• Applications Open : Rolling
• First Reviews Begin : October 2026
• Preferred Start Date : February 2027
• Interviews : Technical interview for shortlisted candidates
• Closing Date : Until the position is filled

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 by email.