Post Doctoral Researcher – Smart Agriculture and Food Security

University of Doha for Science and Technology (UDST) is the first national applied University in the State of Qatar, offering applied Bachelors and Master’s degrees in addition to certificates and diplomas in various fields. UDST has over 50 programs in the fields of Engineering Technology and Industrial Trades, Business Management, Computing and Information Technology, Health Sciences, Continuing and Professional Education and more.   With more than 600 staff and over 7,000 students, UDST is the destination for top-notch applied and experiential learning. The University is recognized for its student-centered learning and state-of-the-art facilities. Our faculty are committed to delivering pedagogically-sound learning experiences with the incorporation of innovative technological interventions, to further enhance students’ skills and help develop talented graduates that can effectively contribute to a knowledge-based economy and make Qatar’s National Vision 2030 a reality.   The Applied Research, Innovation and Economic Development Directorate invites applications for the position of Post Doctoral Researcher to work on of the UDST Food Security Project and the research initiatives of the UDST Center of Excellence in Food Security and Sustainability.   Application procedure The application should be in English. The application should be sent electronically and be attached as PDF-files, as below. Maximum size for each file is 10 MB. 1. CV:(Please name the document as: CV, Surname, Ref. number) including:

• A complete list of publications
• Previous research experiences
• Two references who can be approached to write letters of reference/recommendation

2. Cover letter: (Please name the document as: Personal letter, Family name, Ref. number) 1-3 pages where you:

• Introduce yourself
• Describe your previous research fields and main research results
• Relate your qualifications, experience, and skills with the job requirements
• Describe your future goals and future research focus

Responsibilities: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: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

• Significant experience in smart precision technologies and agriculture practices
• A good publication record of the position-related papers published in peer-reviewed journals
• Ability to work effectively as a part of a multi-disciplinary research team, and to carry out independent individual research to meet project goals
• Advanced written and oral communication skills
• Experiences in scientific writing for preparation of scientific papers for publication and presentations for meetings and/or conferences
• Outstanding hands-on skills, strong computational and problem-solving skills, and a high level of analytical ability
• Soil, water, and crop data collection, analysis, and interpretation
• Willingness to cooperate and support other researchers in the research lab
• Experience in working with teams of engineers and supervising undergraduate students

Skills Skills in system automation, the use of microprocessors, analysis of meteorological data, climate-change modeling, design, development, and evaluation of agricultural best management practices, precision agriculture, mechanized farming, greenhouse control systems, and resource (soil, water, nutrient, and soilless media) optimization, and the use of GIS and Statistical Software for data analysis.