Research Associate
Full time
at Khalifa University
in
UAE
Posted on January 17, 2025
Job details
Organisation/Company: KHALIFA UNIVERSITY Research Field: Computer Science Engineering Researcher Profile: Recognised Researcher (R2), Established Researcher (R3) Country: United Arab Emirates Application Deadline: 31 May 2027 - 00:00 (UTC) Type of Contract: Permanent Job Status: Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No
Offer Description
Job Purpose- To provide high quality research support and undertake internationally competitive research aimed at publication thereby contributing to the academic and research mission of the University.
- Ability to present research plans and findings in a convincing style, both in oral and written modes of communication.
- Engages in scientific research as a professionally accountable member of the research team, actively participating in problem definition, planning, and execution of the research approach. In addition, undertakes responsibilities such as conducting experiments, interpreting findings, documenting and reporting results and progress, maintaining meticulous research records, and ensuring compliance with KU policies and procedures.
- Conducts regular meetings with the Supervisor to convey research outcomes, review project focus, address concerns, troubleshoot issues, and effectively manage expectations.
- Ensures timely publication of research results in leading journals within the field and presents findings at relevant meetings.
- Takes on the role of mentoring graduate and/or undergraduate students.
- Contributes proactively to the overall activities of both the research team and the Center.
- Undertakes additional responsibilities as assigned by the Advisor.
- Provision of a dataset focusing on Indoor Farms Plants Diseases, shared with an Industry Partner and integrated into the VRI database, enhancing accessibility and utilization across the agricultural community.
- Development of a demonstration showcasing the application of robotics in monitoring crop diseases, utilizing cutting-edge technologies for real-time assessment and intervention.
- Application of deep learning algorithms for Ripeness Monitoring and Disease Identification of Plants in Indoor Farms, offering a sophisticated and accurate method for early detection and analysis.
- Provision of resources and benchmarks tailored for agricultural imagery-based pattern analysis, contributing to the advancement of methods for recognizing and understanding patterns in agricultural landscapes.
- Exploration of efficient data sampling methods, data fusion techniques for multi/hyper-spectral image data, and the development of self, semi, and weakly supervised methods for greenhouse imagery, ensuring robust pattern recognition even in conditions of noise, sparsity, and imbalanced annotations.
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