Fully Funded PhD Positions in AI for Agriculture at New Mexico State University, USA – Spring or Fall 2027

The Niu Lab at New Mexico State University (NMSU) is inviting applications from highly motivated prospective PhD students interested in advancing the use of artificial intelligence, machine learning, robotics, computer vision and related technologies in agriculture. The fully funded PhD positions provide an opportunity for students to undertake interdisciplinary research at the intersection of artificial intelligence and agricultural applications, with an anticipated start in Spring or Fall 2027. The opportunity is particularly suitable for graduates in computer science, engineering, agricultural engineering and closely related disciplines who want to pursue advanced research focused on developing intelligent technologies for productive and sustainable agriculture. According to the opportunity announcement, the Niu Lab is recruiting multiple PhD students to join NMSU's Department of Computer Science and work across artificial intelligence, machine learning, deep learning, computer vision, robotics and agriculture. Overview of the PhD Opportunity The positions are hosted by the Niu Lab at New Mexico State University and will be supervised by Dr. Haoyu Niu. The selected PhD students will conduct research in an interdisciplinary environment, working with several NMSU units and agricultural research partners. The research brings together computer science and agricultural applications, creating opportunities for students to work on technologies capable of addressing real-world challenges in modern agriculture. The anticipated programme start is Spring or Fall 2027, depending on the position and successful applicant. What Makes These PhD Positions Fully Funded? One of the major advantages of the opportunity is that each position comes with several forms of financial and academic support. Successful PhD students will receive: • A stipend to support their graduate studies and living expenses. • Tuition coverage, helping reduce the financial burden associated with pursuing a doctoral degree. • Health insurance as part of the funding package. • Conference travel support, providing opportunities for students to participate in academic and research conferences and share their work with the wider scientific community. The attached announcement specifically states that each position includes a stipend, tuition coverage, health insurance and conference travel support. This combination makes the opportunity particularly attractive to students seeking a funded doctoral research pathway in artificial intelligence, robotics, computer vision and agricultural technology. Research Areas Covered by the PhD Positions The PhD positions focus on several closely connected areas of artificial intelligence and agricultural technology. Applicants are expected to have an academic background or strong research interest in at least one of the listed areas. 1. Artificial Intelligence and Machine Learning Students may conduct research involving: • Artificial intelligence • Machine learning • Deep learning • Reinforcement learning These areas can be applied to agricultural problems requiring intelligent prediction, analysis, automation and decision-making. Applicants with previous research experience in AI or machine learning are therefore well positioned to demonstrate their suitability for the positions. 2. Computer Vision and Multimodal Sensing Another major research area is computer vision and multimodal sensing. This area is relevant to agricultural systems that need to collect and interpret visual or other forms of sensor information. Students interested in using cameras, sensors and AI-based analysis to understand agricultural environments may find this research direction particularly relevant. 3. Robotics and Autonomous Systems The positions also cover robotics and autonomous systems, including: • Unmanned aerial vehicles • Unmanned ground vehicles • Autonomous navigation • Robotic agricultural applications This research area provides opportunities to explore how intelligent machines can operate in agricultural environments with reduced human intervention. 4. Digital Twins and Agricultural Simulation Another research direction is digital twins and simulation for agricultural systems. Students working in this area may explore computational representations and simulations of agricultural systems as tools for research, analysis and decision-making. The announcement identifies AI and machine learning, computer vision and multimodal sensing, robotics and autonomous systems, and digital twins and simulation for agricultural systems as key areas of research interest. Interdisciplinary Research Environment The PhD students will not work exclusively within one academic discipline. Under the supervision of Dr. Haoyu Niu, students will collaborate with: • NMSU's Institute for Applied Practice in AI and Machine Learning (IAAM) • The College of Agricultural, Consumer, and Environmental Sciences • The NMSU Agricultural Experiment Station • Researchers and collaborators working across artificial intelligence and agriculture This interdisciplinary structure is important because the research is designed to connect advanced computational technologies with practical agricultural challenges. Students will therefore have the opportunity to work at the intersection of technical research and agricultural applications rather than studying artificial intelligence in isolation. Qualifications and Eligibility Requirements Applicants should have an appropriate academic background and demonstrate an interest in research relevant to the positions. Academic Qualifications Applicants should hold a: • B.S. degree, or • M.S. degree in one of the following areas: • Computer Science • Computer Engineering • Electrical Engineering • Electronic Engineering • Mechanical Engineering • Agricultural Engineering • A closely related field The announcement specifically requires a B.S. or M.S. degree in one of these disciplines or a closely related area. Research Background or Interest Applicants should have an academic background or strong research interest in one or more of the following: • Artificial intelligence • Machine learning • Deep learning • Reinforcement learning • Computer vision • Multimodal sensing • Robotics • Autonomous systems • Unmanned aerial vehicles • Unmanned ground vehicles • Digital twins • Agricultural systems simulation Having relevant research experience can help demonstrate an applicant's preparedness for doctoral-level research. Programming Skills Proficiency in C++ or Python is required for the positions. Experience with ROS or ROS 2 is described as desirable. This means applicants with programming experience in Python or C++, particularly those who have applied these skills to artificial intelligence, robotics, computer vision, machine learning or related research projects, may be well suited to the opportunity. Applicants with experience using ROS or ROS 2 can also demonstrate additional preparation for research involving robotics and autonomous systems. Willingness to Conduct Field Experiments The PhD positions are not limited to laboratory or computer-based research. Applicants must be willing to conduct field experiments. This requirement is particularly relevant because the research is connected to agriculture and may involve testing AI-enabled systems, sensing technologies, autonomous platforms or other technologies in real agricultural environments. Prospective students should therefore be comfortable with an interdisciplinary research approach that may combine: • Computational research; • Programming and AI development; • Data collection and analysis; • Robotics or sensing technologies; and • Agricultural field experiments. Communication and Teamwork Skills Applicants should also possess: • Strong written communication skills; • Strong oral communication skills; and • The ability to work effectively as part of an interdisciplinary research team. These skills are important for PhD researchers because doctoral research often involves collaboration with researchers from different academic backgrounds, presentation of findings, preparation of research publications and communication of technical results. About the Principal Investigator The positions are supervised by Dr. Haoyu Niu, who earned his Ph.D. in Electrical Engineering and Computer Science from the University of California, Merced, in 2022. His research is focused on the intersection of: • Artificial intelligence; • Computer vision; • Autonomous robotics; and • Precision agriculture. According to the announcement, his research has a particular focus on AI-enabled robotic systems for crop monitoring. His work integrates: • Vision-based sensing; • Autonomous navigation; • Sensor fusion; and • Intelligent decision-making. The overall objective is to develop practical technologies that can contribute to productive and sustainable agriculture. This research profile makes the PhD positions particularly relevant to applicants who want to pursue careers in AI-enabled agriculture, agricultural robotics, precision agriculture, autonomous systems, computer vision or intelligent sensing. How to Apply for the Fully Funded PhD Positions Interested applicants are required to submit their application materials directly by email to Dr. Haoyu Niu. Applicants should prepare the following: 1. Curriculum Vitae Submit an updated CV highlighting relevant: • Academic qualifications; • Research experience; • Programming skills; • Publications; • Projects; • Technical skills; and • Other experience relevant to AI, robotics, computer vision, machine learning or agriculture. 2. Academic Transcripts Applicants should provide their academic transcripts as part of the application. These should demonstrate their academic preparation for doctoral-level research. 3. Samples of Relevant Work Applicants should also provide samples demonstrating their previous research or technical work. Examples specifically mentioned in the announcement include: • Publications; • Project reports; and • A portfolio. Applicants should therefore select work that demonstrates their capabilities and research interests in areas relevant to the PhD positions. The application materials should be emailed to Dr. Haoyu Niu at hniu@nmsu.edu. Required Email Subject Line Applicants should use the following format as the email subject: “Your Name_PhD Application” Using the requested subject line can help ensure that the application is clearly identified and processed appropriately. Application Deadline There is no fixed application deadline stated in the announcement. Instead, applications will be reviewed as they are received until all positions are filled. This makes early application particularly important. Interested candidates should prepare their materials and submit them as soon as possible rather than waiting for a conventional closing date. Because positions may be filled progressively, applicants should not assume that applications submitted later will receive the same consideration as those submitted earlier. APPLY HERE Selection and Interview Process Applications will be reviewed on a rolling basis. After the initial review, shortlisted applicants will be invited to a virtual interview. Applicants should therefore be prepared to discuss: • Their academic background; • Previous research experience; • Relevant programming skills; • Research interests; • Interest in AI and agriculture; • Relevant publications or projects; • Potential research directions; and • Their ability to work within an interdisciplinary research team. Who Should Consider Applying? These PhD positions may be particularly suitable for graduates who want to build advanced expertise in the application of artificial intelligence to agriculture. Potential applicants include graduates interested in: • A

• Hold a B.S. or M.S. degree in Computer Science, Computer Engineering, Electrical Engineering, Electronic Engineering, Mechanical Engineering, Agricultural Engineering, or a closely related field • Have academic background or strong research interest in artificial intelligence, machine learning, deep learning, reinforcement learning, computer vision, multimodal sensing, robotics, autonomous systems, unmanned aerial vehicles, unmanned ground vehicles, digital twins, or agricultural systems simulation • Demonstrate proficiency in C++ or Python programming • Experience with ROS or ROS 2 is desirable • Be willing to conduct field experiments • Possess strong written communication skills • Possess strong oral communication skills • Demonstrate ability to work effectively in teams