University of Doha for Science and Technology (UDST) was officially established by the Emiri Decision No13 of 2022, and it is the first national university specializing in academic applied, technical, and professional education in the State of Qatar. UDST has over 70 bachelor's and master's degree programs, diplomas, and certificates. The university houses 5 colleges: The College of Business, the College of Computing and Information Technology, the College of Engineering and Technology, the College of Health Sciences, and the College of General Education, in addition to specialized training centers for individuals and companies. UDST is recognized for its student-centered learning and state-of-the-art facilities. Its world-renowned faculty and researchers work on developing the students’ skills and help raise well-equipped graduates who proudly serve different sectors of the economy and contribute to achieving human, social, and economic development goals nationally and internationally.

With more than 700 staff and over 8,000 students, UDST is the destination of choice for 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 that incorporate innovative learning technologies.  Our aim is to enhance students’ skills and help develop talented graduates who 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 of UDST invites applications for the position of Research Assistant- IoT and AI to work on project Q-Cattle, an integrated AI and IoT framework for sustainable livestock management to increase the efficiency of milk and meat production in Qatari Market. 

Responsibilities:

• Develop, test, and integrate IoT-based sensor systems for monitoring livestock health, behavior, and environmental impact into a unified platform.
• Support the creation, refinement, and deployment of AI-driven predictive models on embedded systems such as ESP32 and Raspberry Pi to collect, process, and send data to the cloud.
• Monitor real-time data acquisition processes, ensuring data quality, integrity, and seamless synchronization with cloud storage.
• Design, implement, and test a comprehensive dashboard for intuitive data visualization, real-time notifications, and actionable insights for livestock management.
• Analyze historical and real-time data using advanced processing techniques to extract meaningful insights and enable proactive decision-making.
• Draft high-quality research outputs, including Q1 and Q2 journal papers, white papers, book chapters, and technical documentation, showcasing methodologies, findings, and innovations.
• Develop and optimize software and algorithms eligible for copyright and patent protection, focusing on AI models, data analysis tools, and system integration.
• Collaborate with industry partners and stakeholders to implement IoT and AI-driven solutions, conduct farm-based assessments, and evaluate real-world applications of the system.

Qualifications and Skills:

Essential:

• Bachelor, Master or P.hD degree in Electrical Engineering, Computer Science, Data Science, or a related field.
• Knowledge of IoT systems, sensor integration, and data processing pipelines.
• Proficiency in AI/ML tools and techniques, especially in predictive modeling.
• Familiarity with livestock farming or agricultural technologies.
• Experience in data visualization and dashboard development (e.g., Power BI, Tableau, or custom platforms).
• Strong analytical and problem-solving skills.

Preferred:

• Experience in collaborating with multidisciplinary teams and industry partners.
• Knowledge of environmental metrics and greenhouse gas monitoring.

Competencies:

• Excellent communication and organizational skills.
• Ability to work independently and as part of a team.
• Strong commitment to research ethics and quality.
• Attention to detail and a proactive approach to problem-solving