Graduate Student – EO-Based and Knowledge-Guided ML Crop Forecasting

Graduate Student – EO-Based and Knowledge-Guided ML Crop Forecasting - Research Affiliate Program

Agriculture and Agri-Food Canada - Science and Technology Branch

Closing date : February 2, 2026 - 23 : 59, Pacific Time

Reference number

AGR26J-059095-000215 Selection process number

26-AGR-RAP-4 Location

Charlottetown (Prince Edward Island) Employment tenure

The projected start date is May 1, 2026 with an end date of August 28, 2026 (or depending on the candidate's availability or needs). It is expected that the student will work 37.5 hours per week with the possibility of part-time extension. 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.To be considered for Research Affiliate Program (RAP) work opportunities, all candidates must meet the following eligibility criteria by the date of appointment :

• Be recognized as having full-time student status at an accredited Canadian post-secondary academic institution (this includes students with a disability deemed to have full-time status). Individuals pending approval of acceptance or in the process of submitting applications are encouraged to apply, as proof of enrollment will only be required prior to the start date.
• 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

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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 :

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.

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

Candidates will be required to pay for their own travel related to assessment and successful candidates will be responsible for obtaining their own living accommodations.Successful completion of both a RAP work assignment and your educational program may lead to a temporary or permanent federal public service position for which you meet the merit criteria and conditions of employment.

Conditions of employment

Reliability Status security clearance - Each student hired through the Research Affiliate Program (RAP) must meet the security requirements of the position as a condition of employment and, therefore will be asked by the hiring organization to complete security-relevant documents.

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You need (essential for the job)

Your application must clearly explain how you meet the following Education :

The candidate must be currently enrolled, or enrolled by the date of appointment, in a master’s or doctoral program in a relevant discipline (e.g., Biological Sciences, Agronomy, Environmental Science, Geography, Engineering, Bioresource Engineering, Computer Science, Remote Sensing, Data Science, or a related field) at an accredited Canadian post-secondary institution. This includes students who have completed a bachelor’s degree and have been admitted to a graduate program starting in the upcoming academic year.

The program must include a research component as part of the curriculum.Note : Candidates must be recognized as having full-time student status at the institution where they are currently enrolled. Individuals pending acceptance or in the process of submitting an application are encouraged to apply. Proof of enrollment will be required prior to the start date.

The student is expected to relocate or work in Prince Edward Island (PEI) for the duration of the appointment.

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Experience

Experience analyzing environmental or agricultural systems data using programming tools (e.g., Python, R, or GIS software), including spatial, temporal, or remotely sensed datasets.

Experience integrating data or methods from more than one domain (e.g., environmental science, agronomy, remote sensing, or machine learning) within an academic or research project.

Experience working on research projects, including following protocols, collecting, cleaning, and documenting data.

Experience writing reports, research summaries, or essays for academic or professional purposes.

Experience collaborating in multidisciplinary teams, including students, researchers, or external stakeholders.

Knowledge of plant biology, crop growth, and agroecosystems.

Knowledge of remote sensing, Earth Observation, or geospatial data processing.

Knowledge of machine learning or statistical modeling applied to environmental, agricultural, or geospatial data.

Knowledge of research methods, data collection, and data management.Competencies :

Planning and organization skills.

Interactive communication.

Initiative and self-motivation.

Attention to detail.

Teamwork and collaboration.Abilities :

Ability to analyze and interpret complex datasets.

Ability to apply computational and statistical methods to model crop growth and yield.

Ability to synthesize results and communicate findings to both technical and non-technical audiences.

Language requirements (essential for the job)

Applied / assessed at a later date English essential - You are entitled to participate in the selection process in the official language of your choice.

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Our commitment

We're committed to providing an inclusive and barrier-free work environment, starting with the hiring process. If you need to be accommodated during any phase of the evaluation process, please below to request specialized accommodation. All information received in relation to accommodation will be kept confidential.

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Equity, diversity and inclusion

The Public Service of Canada is committed to building a skilled and diverse workforce that reflects the population it serves. We promote employment equity and encourage you to if you belong to one of the designated employment equity groups when you apply.

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Selection may be limited to members of the following employment equity groups : Indigenous (Aboriginal) peoples, persons with disabilities, visible minorities, and women.

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