Post Doctoral Fellow in Learning-based Control for Controlled Environment Agriculture (Mechanical Engineering)

Clemson University MBA Programs; Clemson (SC); On-site; CAD 71,000 - 92,000; Full time

Job summary

Clemson University invites applications for a Post Doctoral Fellow in Velni Lab, Mechanical Engineering. This is a full-time appointment with primary duties including conducting research, writing technical papers, and assisting in proposal development.

The successful candidate will develop learning-based algorithms to control greenhouse environmental conditions (lighting and CO2), collaborate across AI and plant science disciplines, and contribute to publications and grant proposals.

Qualifications

• PhD in a related field with strong research publications.
• Experience in automation and control for agriculture preferred.
• Knowledge of plant sciences and computational methods is advantageous.

Responsibilities

• Develop learning-based algorithms and tools to control greenhouse environmental conditions.
• Conduct research, analyze data, and write technical papers.
• Assist in proposal development and contribute to ongoing projects.

Skills

Automation

Control systems

AI applications

Education

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

Job description

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