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Climate Risk Lab Data Science and Machine Learning Fellowship 2027 for recent PhD, MSc, or first-class graduates.

by October 19, 2026

written by October 19, 2026 0 comments

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16 Application Deadline: Ongoing.

Applications are now open for the **2026 Climate Risk Lab Data Science and Machine Learning Fellowship.**The Climate Risk Lab is looking to recruit recent PhD, MSc, or first-class Honours graduates with backgrounds in data science, machine learning, computer science, mathematics, statistics, econometrics, engineering, physics or related fields, who are excited to apply their expertise at the intersection of climate science and solutions.

The goal of this Fellowship is to both accelerate the capacity of the lab to deliver breakthrough science using the latest tools, and to strengthen the research and data science skills of successful candidates.

This role entails the provision of data science support to an interdisciplinary team of research staff, projects and other activities within the Climate Risk Lab, working largely with climate, environmental, health, and socioeconomic data. Fellows will also be encouraged to lead on a project that is meaningful to them.

Fellows will be paired with a senior data scientist or postdoctoral researcher to provide mentorship support during their position and will have the opportunity to lead data intensive research within the lab and to participate in working group meetings with international collaborators and/or conferences.

This is a full-time, one-year fellowship with opportunity for a one-year renewal based on candidate performance. There may also be potential for the Fellowship to be turned into a PhD scholarship or Postdoc funding. Fellows will be based in Cape Town at the Climate Risk Lab.

Responsibilities include
● Analysis of big climate, environmental, health, and/or socioeconomic datasets;
● Developing and applying AI tools to enhance data science workflows and supporting other analysts to use AI tools;
● Downloading, cleaning, processing, and optimising large datasets;
● Mainstreaming best practices for reproducible workflows, including contributing to and maintaining Git repositories;
● Documenting and communicating workflows;
● Development and maintenance of code to improve the efficiency of processing and analysing large spatial-temporal datasets, including parallel computing approaches;
● Leading or co-leading a research project towards a peer-reviewed publication, toolkit, or dataset;
● Availability to travel internationally if needed.

Minimum criteria
● MSc degree or first-class Honours degree (completed or in final year) in data science, machine learning, computer science, mathematics, statistics, or related fields;
● Proficiency in programming languages, preferably Python or R;
● Solid grounding in statistics and/or data science principles;
● Excellent oral and written communication skills.

Nice-to-haves● Experience with R and Python packages such as xarray, pandas, numpy, zarr, netCDF4, terra, tidyverse, arrow, or duckdb;
● Familiarity with bash, Linux, and working in HPC environments;
● Experience with agentic AI tools;
● Experience using GitHub for code sharing and collaboration;
● Experience with large spatial-temporal datasets.

Remuneration
The remuneration range for this role will be competitive with academic and market rates and based on candidate experience.

Application Document
Documents to be submitted as a single PDF File
● CV, including statement of whether you hold a valid South African work or study permit;
● Name, position and email address of two professional referees included in your CV;
● Cover letter describing why you are a good fit for the Fellowship;
● Academic transcript;
● Links to GitHub, Jupyter notebooks, or other examples of written code (optional);
● Personal website, and/or LinkedIn profile (optional).

For More Information:

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