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Christopher Lawley

3 days ago

MSc in Large-scale Data-Driven Mineral Prospectivity Mapping at Carleton University (Earth Sciences, Data Science, Geoscience) Carleton University in Canada

Degree Level

Master's

Field of study

Computer Science

Funding

The position offers a scholarship of approximately $25,500 in the first year and $27,500 in the second year, totaling $53,000, divided into three equal installments each year. The student is expected to work approximately 10 to 15 hours per week for the Research Affiliate Program period. No information on tuition coverage is provided.

Deadline

Mar 27, 2026

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Country

Canada

University

Carleton University

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Keywords

Computer Science
Data Science
Geology
Environmental Science
Mathematics
Earth Science
Geographic Information System
Statistics
Geostatistic
Machine learning

About this position

Carleton University is offering two MSc funding opportunities in Earth Sciences, focusing on large-scale data-driven mineral prospectivity mapping. These projects are in collaboration with Natural Resources Canada (NRCan) and aim to advance Canadian methodologies for mineral potential modeling. The research will involve geoscientific data analysis, machine learning, statistics, and geodata science, with the goal of developing reliable and objective prospectivity products for critical mineral exploration.

The MSc projects are ideal for students with backgrounds in Earth Science, Data Science, Geostatistics, Computer Science, Geology, or related fields. No prior experience with AI or geoscience is required, and the projects can be tailored to the candidate's research interests and experience. The work environment includes collaboration with a dynamic geoscience team at the Geological Survey of Canada, supporting the Critical Mineral and Geoscience Data Program.

Applicants must have a Bachelor of Science degree with a thesis in a relevant field and experience with spatial data, machine learning, statistics, mathematics, and effective communication in English. The scholarship provides approximately $25,500 in the first year and $27,500 in the second year, totaling $53,000, with an expectation of 10 to 15 hours of work per week under the Research Affiliate Program. The deadline to apply is March 27, 2026, with the program starting in September 2026.

To apply, submit your résumé and a cover letter outlining your qualifications and interest in the position. Proof of education credentials is required. The selection process values equity, diversity, and inclusion, and encourages applications from underrepresented groups. For more information, visit the provided links or contact the supervisor.

Funding details

The position offers a scholarship of approximately $25,500 in the first year and $27,500 in the second year, totaling $53,000, divided into three equal installments each year. The student is expected to work approximately 10 to 15 hours per week for the Research Affiliate Program period. No information on tuition coverage is provided.

What's required

Applicants must be enrolled or eligible to enroll in an MSc program at Carleton University with a specialization in Earth Science, Geographic Information Systems, or a relevant field. A Bachelor of Science degree with a thesis from a recognized institution in Data Science, Geodata Science, Geostatistics, Computer Science, Geology, Earth Sciences, Geosciences, or a related field is required. Experience with spatial data (geodata), machine learning, statistics, mathematics, and effective communication in English is essential. Experience working individually and in teams is also required. Proof of education credentials and a list of courses may be required. Candidates must be able to obtain Reliability Status security clearance and be eligible to work in Canada.

How to apply

Submit your application through the provided link. Include your résumé and a cover letter detailing your interest and how you meet the essential qualifications. Ensure you provide proof of education credentials. Contact the supervisor for more details if needed.

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