Graduate Student

**Reference number**
AGR26J-059095-000215

**Selection process number**
26-AGR-RAP-4

**Location**
Charlottetown (Prince Edward Island)

**Employment tenure**

**Salary**
$25.17 to $38.38 per hour - Masters $25.17 to $31.69 and PhD $29.64 to $38.38. Varies as per the level of education and experience.

**Who can apply**
Persons residing in Canada, Canadian citizens, and Permanent residents abroad.
- Be enrolled in an academic program that requires research as part of the curriculum.
- Be at least the minimum age to work in the province or territory where the job is located.

**Organization information**
For further information on the organization, please visit Agriculture and Agri-Food Canada.

**About the position**:
**Duties**
The successful applicant will work on AAFC’s national, multidisciplinary project aimed at developing a next-generation forecasting system for potato yield using a knowledge-guided machine learning (KGML) approach that integrates Earth Observation (EO) data, process-based crop modeling, and field-scale observations into a unified forecasting framework. The research will examine how environmental, soil, and management factors influence potato yield across multiple production systems and varieties.

The student will contribute to developing and validating predictive models, with opportunities to gain experience in EO-based analyses and interpret field data collected from potato farms in Prince Edward Island and other regions. The student will complete this research as part of their MSc or PhD thesis, or as part of a last-year undergraduate summer research project, providing a strong foundation for future graduate studies.
The student will:

- Develop and train deep learning and KGML models to predict potato growth and yield using synthetic crop model outputs and field-scale observations.
- Fine-tune models with observed yield data stratified by production system, region, and irrigation type.
- Conduct statistical analyses to evaluate and validate model predictions.
- Gain exposure to EO-derived indicators provided by the EO team as model inputs.
- Work semi-independently to troubleshoot models, interpret results, and solve research problems.
- Interact with a multidisciplinary team of scientists, students, and research technicians across Canada.
- Review relevant scientific literature and interpret results in the context of Canadian potato production systems.
- Contribute to scientific reports and publications, and present findings in written and oral formats at conferences.
- Work in Dr. Morteza Mesbah’s data science lab, using modern computational resources for model development.
- Participate in optional field visits in PEI to observe potato production and data collection practices, and training in ML and EO-based methods.
- Follow best practices for research ethics, data management, and reproducibility.

**Work environment**
The student will work in a collaborative, interdisciplinary team with scientists from across Canada. The position is primarily office
- and computational-based, with optional exposure to field data. The student will work semi-independently in a research environment and interact with colleagues from multiple disciplines, including students, research technicians, and scientists.

AAFC is committed to diversity and inclusion. We have several networks dedicated to ensuring that the department continues to grow as an inclusive, accessible, respectful and diverse workplace. All employees are encouraged and welcomed to join the networks and participate in their activities and events.
- The Gender and Sexual Diversity Inclusiveness Network
- The Indigenous Network Circle
- The Managers' Community
- The Persons with Disabilities Network
- The Student Panel of Representatives for Orientation, Unity and Training
- The Visible Minorities Network
- The Women in Science, Technology, Engineering and Mathematics Network
- The Young Professional's Network

**Intent of the process**
This project is part of a national, interdisciplinary initiative led by AAFC to advance potato yield forecasting using knowledge-guided machine learning (KGML), process-based crop modeling, and Earth Observation (EO) data. The research generates new scientific knowledge and predictive tools to improve the accuracy, resolution, and operational use of potato yield forecasts, supporting growers and decision-makers across Canada.

The position is primarily office
- and computational-based, with opportunities to gain experience using field data collected from potato farms in PEI and other regions. The successful student will work semi-independently in a research environment and will interact with individuals from a wide range of backgrounds, including students, research technicians, and scientists from multiple disciplines.

**Positions to be filled**
1

**Important messages**

Successful completion of both a RAP work assignment and your educational program may lead to a temporary or permanent federal pub