Assistant Specialist - Controlled Environment Engineering (CEE) Lab

NATURE AND PURPOSE:

The position of Specialist has a narrow focus in a specialized area and provides technical or specialized expertise (e.g., with instrumentation and research equipment or with social science research methods) in the planning and execution of a research project. The Specialist stays apprised of emerging issues and problems and maintains technical competence in the designated area(s) of specialization. Within this defined area and when appropriate, the Specialist is expected to provide leadership, facilitate teamwork and develop collaborative relationships with colleagues and to supply input into the planning of research and educational programs. Normally, Specialists do not have Principal Investigator (PI) status but may obtain permission by exception and/or collaborate with a PI in preparing research proposals for extramural funding. The Specialist is evaluated for merit and promotion using three basic criteria outlined below.

MAJOR RESPONSIBILITIES AND DESIGNATED AREAS OF EXPERTISE:

I. RESEARCH IN SPECIALIZED AREAS (88% EFFORT)

The Assistant Specialist will assist in developing and implementing AI-driven models, simulations, and data-driven decision-making tools for controlled environment agriculture (CEA). Key responsibilities include:

a. Supporting research in machine learning, cyber-physical systems, and digital twins for indoor farming applications.

b. Conducting data collection, processing, and analysis for AI-based control and monitoring systems.

c. Developing and maintaining sensor networks, data acquisition systems, and control algorithms for optimized crop production.

d. Assisting in preparing scientific publications, research reports, and grant proposals.

e. Collaborating with faculty, researchers, and students to advance research goals.

II. PROFESSIONAL COMPETENCE AND ACTIVITY (6% EFFORT)

a. Participate in relevant AI, machine learning, and controlled environment agriculture professional societies, technical groups, and research organizations.

b. Review research proposals, journal manuscripts, and publications on AI-driven crop modeling, digital twins, and cyber-physical systems in indoor farming.

III. UNIVERSITY AND PUBLIC SERVICE (6% EFFORT)

a. Maintain liaison and collaborate with industry partners, government agencies, and external stakeholders on topics related to artificial intelligence in controlled environment agriculture.

b. Participate in departmental, college, and university committees, contributing expertise in AI applications for indoor farming and resource optimization.

Education and Expertise:

a. Education: MS degree in Computer Science and Engineering, Data Science, and Electrical Engineering

b. Research & Technical Skills: Experience in machine learning, AI modeling, and cyber-physical systems for controlled environment agriculture.

c. Programming Skills: Proficiency in Python, MATLAB, or other programming languages used in AI modeling and simulation.

Data Acquisition & Sensors: Experience with sensor integration, data collection, and calibration (LabVIEW experience is preferred).

d. Communication & Collaboration: Strong writing and oral communication skills, with the ability to contribute to research papers and grant proposals.

Lab: https://ceelab.ucdavis.edu/

• Master of Science Degree in Statistics
• Experience Developing and Training Machine Learning Models
• Experience Processing Data to Model Crop Growth

• Completed Coursework in Statistical Methods for Research, Statistical Data Science, Computational Statistics, or Other Relevant Courses.

As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct, are currently being investigated for misconduct, left a position during an investigation for alleged misconduct, or have filed an appeal with a previous employer.

• "Misconduct" means any violation of the policies or laws governing conduct at the applicant's previous place of employment, including, but not limited to, violations of policies or laws prohibiting sexual harassment, sexual assault, or other forms of harassment, discrimination, dishonesty, or unethical conduct, as defined by the employer.
• UC Sexual Violence and Sexual Harassment Policy
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• APM - 035: Affirmative Action and Nondiscrimination in Employment

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