Country:United States of AmericaLocation:CAG10: ALC HQ 1025 Cobb Place Blvd, Kennesaw, GA, 30144 USACarrier 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 shape 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 Computational Engineering group within Systems & Controls COE (Carrier WHQ) has responsibility for developing and deploying model-based methods and tools for design and operation of Carrier products. This includes using mathematical models for taking product design decisions, quantification of system uncertainty, and securing efficient operation in the field. The core technology areas of the group are large-scale continuous and discrete optimization, variability analysis, data analytics, machine learning, numerical and algorithm analysis, as well as understanding requirements for mathematical models to be reliably used by numerical algorithms. The group also develops and maintains computational platforms and tools for deployment to Carrier product teams. Engineers in the group work on global projects in conjunction with Carrier business units and renowned external research organizations (universities and research institutes) to create worldwide business impact through new product and process innovations.About this roleRecognized Systems Engineering master. Leads the development of work methods, develops overall systems strategy, provides support and guidance to various technical teams, and approves common work practices.Position Summary:The candidate will provide technical leadership in the area of numerical optimization of thermo-fluid systems which are core to the Carrier business – and in particular design and deployment of on-line supervisory controls.The candidate will engage with global product teams to solicit business needs and convert those into computational decision-making workflows, methods and tools to radically impact how Carrier products are designed, deployed and operated. Key targets include improving engineering effectiveness as well as developing disruptive, innovative methods for model-based design and operation of Carrier systems. The candidate will take active part in product development to support design engineers in adopting and using new methods and tools. The candidate will also work closely with other teams in Systems & Controls COE, including teams responsible for model development (to drive the development of optimization-friendly thermo-fluid models) and controls engineering (to promote the use of computational optimization strategies (MPC, RTO) as needed). In addition, the candidate will contribute to mentoring of junior engineers in the group, supervision of student interns, organization of optimization trainings, and technology roadmap development.Key Responsibilities:
Deployment. Ensure that methods and tools developed in the group impact the Carrier business through engagement in global product projects, including capture and formulation of computational problems arising in such projects that relate to “supervisory” control formulations, trade studies and implementation. Use control theoretic tools for robustness and stability guarantees in addition to performance optimality.
Methods, tools and algorithms. Ensure that appropriate computational methods, tools and algorithms for designing dynamic, optimal supervisory control are based on sound mathematical foundations and are deployed to match the needs of the Carrier business.
Modeling for dynamic supervisory control based on optimization formulations. Support development of mathematical models for thermo-fluid systems are built based on principles and best practices that secure reliable application of numerical optimization algorithms for control applications.
Talent. Support development and training of staff within Carrier product teams and within the Computational Engineering group; contribute to talent pipeline by supervising student internships and these
Required Qualifications:
Master’s Degree in Engineering or Sciences
10+ years in engineering role with a strong element of model-based computation and optimization
Preferred Qualifications:
PhD is preferred
Prefer degree in an Engineering discipline (e.g., applied mathematics, mechanical or chemical engineering)
Proven ability to design on-line closed loop supervisory controllers (e.g., MPC/NMPC), including stability and robustness analysis and deployment to relevant industrial processes.
Proven ability to capture engineering design and operation problems as mathematical programming problems (NLPs and MILPs), including attention to reliable convergence of such problems.
In-depth knowledge and experience with the mathematical theory (applied mathematics, numerical analysis and functional analysis), algorithmic foundations (notably existence and convergence proofs), and methods/tools for numerical optimization (SQP, interior point method, etc.) of large-scale systems.
Experience from using common algorithms/solvers for large-scale gradient-based non-linear programs, e.g., IPOPT, CONOPT, KNITRO, and WORHP, including their respective applicability to different types of problems. Knowledge about discrete optimization formulations, methods, and algorithms is a merit.
Expertise and experience with physics-based modeling principles and best practices of thermo-fluid systems, such as vapor compression cycles or power plants.
Familiarity and experience with development of computational platforms and tools in Python or equivalent.
Familiarity with using HPC and cloud-based platforms for computation at scale.
Demonstrated ability to lead and work as part of a multidisciplinary team and an entrepreneurial attitude towards technological innovation in a global environment.
Self-starter who is well-organized in an international team environment, and has excellent interpersonal, leadership and communication skills.
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Full-time