United States of America
CAF52: Carrier Bloomfield 1300 Hall Boulevard Suite 1A, Bloomfield, CT, 06002 USA
Carrier is a leading provider of heating, ventilating, air conditioning and refrigeration systems, building controls and automation, and fire and security systems leading to safer, smarter, sustainable and high-performance buildings! As Carrier emerges as an independent, standalone company, the Carrier Global Engineering organization is transforming. This new multi-site organization will ensure Carrier’s lead position in the market through investments in advanced research, technologies and methods that will craft the future of our products!
Willis Carrier invented the first modern air-conditioning system in 1902 and changed how people live, work, and play. Today, Carrier needs your talent to build upon this legacy and to deliver what’s next for the modern world.
The candidate will provide broad capability in the use of model-based robust design methods and tools in the newly formed Computational Science & Engineering Group in Carrier Global Engineering.
The candidate will join teams to develop approaches to quantify and control variability in product development. The intent is to acquire problem formulations and solutions that advance the use of Model-Based Development across the Carrier business units and across the product development lifecycle to understand the sources of variability and to provide mitigation mechanisms. The intent is to increase the efficiency of the product development processes at Carrier using models to take design decisions that affect the ability to meet performance targets. The candidate will help identify opportunities where robust design methods can play a key role in extracting information from models (physics based and with empirical model elements) and measured data. The opportunities range from conceptual design and the selection of product architectures that are robust to variability to trade studies to obtain global platforms to control of Carrier products to the use of active control methods to mitigate the risk of not meeting performance targets due to variability.
Key Job Responsibilities:
UQ. Solid understanding of fundamental theory and computational methods to assess uncertainty and ultimately requirement risk using models that are a mix of physics based, empirical elements and measured data.
Knowledge of key processes used to assess and mitigate variability with a focus on Six Sigma, Robust Design Optimization and VMEA. Proven ability to apply the methods and tools in industrial settings.
Data analysis and cleaning from test plans and data acquisition. Ability to guide teams on measurement practices (gage R&R) and test plans (sensing, data recording, analysis of data quality) to assess Cpk for uncertainty quantification studies.
Use of GSA in sources of variability evaluation. Use models to identify causes of variation and find data to validate.
Use of optimization in UQ minimization. Apply means to reduce impact of uncertainties on product risk.
Experience / Qualifications:
Physics knowledge and interest – from a Mechanical Engineering or Chemical Engineering background. Ability to engage with vapor compression systems models (and associated components).
Desire to work with Carrier business unit teams.
Strong math skills – fundamentals (functional analysis, ability to read and assimilate new academic literature, proofs, problem formulations).
“Scientific computing.” Linux, Docker, Matlab, Python, AD, FMU, Modelica. Range of experiences in computing (“computing fabrics”) and use of HPC (cloud, on prem).
MEng or PhD in Engineering with 5-7 years of experience
Carrier is An Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status, age or any other federally protected class.
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