Post Doctoral Fellow in Learning-based Control for Controlled Environment Agriculture (Mechanical Engineering)
- SK, Canada
Full-time
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Job details
- Job type
- Full-time
- Posting language
- English
- Posted
Original 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
Application Instructions
Applications, including a cover letter, full CV (including Google Scholar link), unofficial undergraduate and graduate transcripts, and names and contact info for three references, should be submitted through Interfolio (https://apply.interfolio.com/169674)
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
Application Instructions
Applications, including a cover letter, full CV (including Google Scholar link), unofficial undergraduate and graduate transcripts, and names and contact info for three references, should be submitted through Interfolio (https://apply.interfolio.com/169674)
More about this job
Responsibilities
The fellow will conduct research and develop learning-based algorithms to control lighting and CO2 levels in greenhouses. Additional duties include writing technical papers and assisting in the development of research proposals.
Requirements
Applicants must hold a Ph.D. in Mechanical, Electrical, or Agricultural Engineering or a related field. Prior publications in AI applications for controlled environment agriculture and knowledge of plant sciences are required.
Skills
- Learning-based Control
- Automation
- Artificial Intelligence
- Machine Learning
- Plant Sciences
- Computational Sciences
- Technical Writing
- Proposal Development
Visa sponsorship
Not detected in the job text
Categories
- Science & Research
- Engineering
- Agriculture
- Education
- Environmental & Sustainability
Keywords
- Greenhouses
- Plant Factories
- Mechanical Engineering
- Electrical Engineering
- Agricultural Engineering
- AI
- Machine Learning
- Automation
- Control Systems
- CO2 Control
- Lighting Control
- Computational Science
- Research
- Higher Education