Job Description:

Post-doctoral position at the University of Manitoba

Project title: Machine Learning Development for Crop-Weed Interference Quantification

Opportunities: Post-Doctoral Position, Position # 21688

Join our dynamic team at the Department of Plant Science, Faculty of Agricultural and Food Sciences, University of Manitoba, Canada, led by Dr. Rob Gulden, Dr. Dilshan Benaragama, Christopher Henry and Dr. Nasem Badreldin. We are embarking on an innovative project, "Remote Sensing Weed-Crop Interference and Data-Driven Weed Management Decision-Making Tools," generously funded by NSERC (National Science Engineering Research Council). This exciting initiative uses drone-based optical and non-optical remote sensors (RGB, Multispectral, and LiDAR) to gather data that will be used to model crop and weed growth over the growing season. The remote sensed data requires segmentation into the weed and crop constituents for which we require an enthusiastic PDF who is skilled in image segmentation using machine learning algorithms. From this we will develop precision decision-making models for improved and more sustainable weed management that relies on reduced herbicide use in the main grain crops in Western Canada.

Position Available: Post-Doctoral Fellow (1 term position)

Duration: Up to four years

Remuneration based on qualifications and experience.

Responsibilities:

• Work in a collaborative team environment, under the general direction of Drs. Robert Gulden, Dilshan Benaragama, Christopher Henry, and Nasem Badreldin, to achieve the goals of associated research grant funding requirements;

• Conduct academic literature reviews and associated writing tasks;

• Analyze and pre-process remote sensing drone and satellite data, as well as RGB, multispectral, and LiDAR data for use in machine learning models;

• Design, train, and test deep learning neural network architectures and associated models;

• Draft technical reports;

• Contribute to academic and popular publications related to project research; and

• Other duties as assigned.

Qualifications:

Requirements:

• A a Ph.D. completed within the past five years in computer science, remote sensing, engineering, mathematics, statistics, physics, or related fields;

• Excellent oral and written communication skills;

• Excellent organizational skills with the ability to manage time effectively and efficiently to meet deadlines; and

• Strong interpersonal skills with the ability to work as part of a team.

Desired Qualifications:

• Formal training in machine learning, especially convolutional neural networks and generative methods;

• Experience in developing machine learning applications;

• Experience working with digital images and remote sensing data;

• Experience working with geospatial datasets;

• Knowledge and experience with Python, and TensorFlow or PyTorch;

• Knowledge and experience with containerization frameworks (such as Docker or Singularity); and

• Knowledge and experience with Linux.

Opportunities for Trainees:

• Joining a multidisciplinary team and working with experts in agriculture, plant science, soil science, and computer science

• Gaining broad knowledge of both the computational and agricultural aspects of the digital agriculture field

• Solving problems that directly link machine learning theory to real-world applications

• Making machine learning theoretical contributions

• Gaining machine learning development experience

• Presenting at international conferences

• Publishing in international peer-reviewed journals

Additional Information:

Don't miss this opportunity to be part of groundbreaking research at the forefront of agricultural innovation! Join us as we pave the way for digital and machine learning supported improvements to sustainable weed management practices in Western Canada.

Application Process: Interested candidates are asked to submit a CV and a statement of research interests to digital.weedscience@gmail.com. Applications will be accepted until the position is filled. Only applicants of interest will be contacted.

The University of Manitoba is committed to the principles of equity, diversity & inclusion and to promoting opportunities in hiring, promotion and tenure (where applicable) for systemically marginalized groups who have been excluded from full participation at the University and the larger community including Indigenous Peoples, women, racialized persons, persons with disabilities and those who identify as 2SLGBTQIA+ (Two Spirit, lesbian, gay, bisexual, trans, questioning, intersex, asexual and other diverse sexual identities). All qualified candidates are encouraged to apply; however, Canadian citizens and permanent residents will be given priority.

If you require accommodation supports during the recruitment process, please contact UM.Accommodation@umanitoba.ca or 204-474-7195. Please note this contact information is for accommodation reasons only.

Application materials, including letters of reference, will be handled in accordance with the protection of privacy provision of The Freedom of Information and Protection of Privacy Act (Manitoba). Please note that curriculum vitae may be provided to participating members of the search process.