Funded Masters Position in Machine Learning for Precision Agriculture at University of Maine

Professor Vikas Dhiman, Assistant Professor in the Electrical and Computer Engineering department at the University of Maine, is recruiting a Masters-level graduate research assistant for Fall 2026. The research focuses on developing robust and adaptable machine learning algorithms for precision agriculture, with applications such as predicting water potentials of wild blueberries and detecting leaf chlorophyll concentration using hyperspectral sensors. The position involves working closely with Prof. Dhiman on all aspects of the research process, including literature review, algorithm development (machine learning, perception, control), experiment design, coding, data analysis, and dissemination of results through writing and presentations. Applicants should have a strong background in mathematics (linear algebra, probability theory, control theory, optimization) and programming (Python or C++). Prior experience in machine learning, computer vision, or reinforcement learning is preferred. Candidates must be organized, responsible, proactive, and able to work independently. Eligibility requires acceptance into the MS program in Computer Engineering at the University of Maine. The assistantship offers funding that includes tuition for up to 6 credits per semester, half of the University of Maine graduate health insurance, and a $1,888 monthly stipend for 9 months of the academic year. The position is based in Orono, Maine, USA, and may begin in Fall 2026 or earlier. To apply, send an email to [email protected] with the subject 'MSAgriF26', including a sample project code or a document of a mathematical proof or project report assignment, and your resume/CV. Applicants must also apply to the MS program in Computer Engineering at the University of Maine. For more details, refer to the provided links. Relevant keywords: Machine Learning, Precision Agriculture, Computer Vision, Reinforcement Learning, Electrical and Computer Engineering, Python, C++, Optimization, Control Theory, Hyperspectral Sensor.