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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 12 hours ago
of high functioning campus engineering design standards, design of engineered systems, inspection of all projects implemented within campus buildings, engineering support of Building Services personnel
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of Ohio Board of Pharmacy (BOP), university and COM CSP requirements. The CSPO implements and oversees COM CSP development and enterprise-wide projects including the rollout and optimization of technology
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• Blueprint Reading • Geometric Dimensioning and Tolerancing (GD&T;) • Precision Measurement and Inspection • Machining Math • Cutting Tools, Speeds, and Feeds • Manual Machining Fundamentals • CNC
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Cities. We are seeking a Mechanic 3. This is our third and most senior level mechanic position. Responsibilities include providing routine maintenance, repair, and inspection of building mechanical systems
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(HVAC&R). The position will develop, document and program control strategies for optimizing mechanical systems performance, prepare specifications, manage projects and serve as a technical support
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these components, coordinate their fabrication and integration, and lead the commisioning and optimization of the experimental instrument. Your work will span electron-optical design, electromagnetic and ray-tracing
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develops, you will also help establish the experimental imaging methodology and optimize the microscope for diffractive imaging. This position is suited to candidates interested in building new
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assignments and assists the Controls Supervisor with system upgrades, commissioning, and optimization projects. Responsibilities Install, program, calibrate, maintain, and repair building automation systems
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Position Details Position Information Job Title Controls Technician I Position Number 400560 Vacancy Open to All Candidates Time-Limited No Department Homepage http://www.ecu.edu/cs-admin
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, such as statistical learning, causal inference, experimental design, probability, stochastic PDEs, algebraic statistics, normalizing flows, scientific machine learning, optimization, inverse problems