Post Doctoral Fellow in Learning-based Control for Controlled Environment Agriculture

Clemson University; Clemson (SC); On-site; USD 52,000 - 68,000; Full time

Job summary

Clemson University is seeking a Post Doctoral Fellow in Learning-based Control for Controlled Environment Agriculture to advance research in greenhouse automation. The role focuses on developing learning-based control methods to optimize lighting and CO2 conditions, with emphasis on AI and machine learning applications in agriculture.

Successful candidates will have a Ph.D. in a related engineering field and experience in automation, controls, and plant sciences.

Qualifications

• Ph.D. in Mechanical Engineering, Electrical Engineering, Agricultural Engineering, or related field.
• Experience with automation, controls, and AI applications in controlled environment agriculture.
• Strong knowledge of plant sciences and computational sciences (controls and machine learning).
• Hands-on greenhouse or plant factory experience preferred.

Responsibilities

• Conduct research and write technical papers.
• Assist in proposal development.
• Develop learning-based algorithms to control greenhouse environments.
• Other duties as assigned.

Skills

Automation

Control theory

Machine learning

AI applications

Greenhouse environments

Education

Ph.D. in Mechanical Engineering, Electrical Engineering, Agricultural Engineering, or related field

Job description

Post Doctoral Fellow in Learning-based Control for Controlled Environment Agriculture

Clemson University: College of Engineering, Computing and Applied Sciences: Mechanical Engineering

Location: EIB 134
Open Date: Jun 26, 2025

Description

Velni Lab in the Department of Mechanical Engineering at Clemson University invites applications for a Post Doctoral Fellow position. This is a full-time appointment, with main duties of conducting research, writing technical papers, assisting in proposal development, and other duties as assigned. Specific requirements: The assigned work entails developing new learning-based algorithms and tools to control the environmental conditions (in particular, lighting and CO2) in greenhouses. The applicants are expected to have prior knowledge of automation, control and AI applications in controlled environment agriculture demonstrated by prior publications. Strong and fundamental knowledge of plant sciences, as well as computational sciences (e.g., controls and machine learning) are required. Prior hands-on experience in greenhouses or plant factories is desired.

Qualifications

Ph.D. in Mechanical Engineering, Electrical Engineering, Agricultural Engineering, or a related field

Clemson University is an EEO/AA employer. Employment decisions are made without regard to characteristics protected by applicable law including disability and protected veteran status.