Your Commitment
The successful candidate will help in developing smart agriculture practices and technologies to detect plant diseases using machine vision technologies. He/she will design, develop, test, and implement a machine vision-assisted artificial intelligence (AI) based prototype variable rate sprayers for targeted applications of fungicides in tomatoes and eggplants in greenhouse and field environments. He/she will be involved in developing variable rate sprayers for targeted applications in young date palm trees, utilizing ultrasonic and other sensors for accurate tree volume calculations and disease management.
The successful candidate will also monitor water, nutrient, and plant characteristics in real time for agriculture systems comprising greenhouse vegetable and date palm production in Qatar. In particular, the potential candidate will support the research toward the development of intelligent technologies to study aspects of food security and natural resource sustainability in the context of Qatar food security and sustainability needs. The incumbent will perform tests using a variety of modern field and laboratory equipment; assess target detection and spraying accuracy, mitigation strategies for climate change, computational and GIS analysis, calibrating and maintaining instrumentation; participating in the collection of the field as well as greenhouse production samples throughout Qatar, assisting in data analysis, manuscript writing, and preparing project reports. The successful candidates will interact with researchers and stakeholders (local and international collaborators and institutions) with respect to the related projects
Qualifications
The candidates must have a PhD in Agriculture/Robotics/Computer/Software/Electrical/Automation Engineering, or closely related disciplines from a renowned university. The potential candidates should have a demonstrated record of scholarship and a track record of excellence in research related to developing agricultural robotics or precision spraying technologies. The candidate should have a solid background in computer programming languages (e.g., Python, CUDA) and machine learning frameworks (e.g., TensorFlow, Pytorch), electronics, instrumentation, and agricultural machinery development. The candidates should have experience with the operation of the laboratory and agricultural equipment (including soil moisture sensors, GPS, drones, stand-alone and networked sensors and weather stations, irrigation systems, image capturing with thermal and infrared cameras, image processing, deep learning, artificial intelligence, and machine vision. Fluency in written and spoken English is required. Fluency in written and spoken Arabic is an advantage.
Preferable research experiences
As the State of Qatar’s premier technical college, College of the North Atlantic – Qatar (CNA-Q) has a special obligation to students and the State. Our mandate, to provide the best hands-on, technical training for this rapidly expanding country, is fulfilled by hiring the best of Canada’s instructors and utilizing world-class facilities to deliver innovative, internationally recognized and responsive technology programs, and making significant contribution to technological development through partnerships with industry.
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