Be a part of our mission! As a world leader in creating comfortable, sustainable, and efficient climate solutions for buildings, homes and transportation, it's our responsibility to put the planet first. For us at Trane Technologies, and through our businesses including Trane\u00AE\u00A0and\u00A0Thermo King, \u00A0sustainability is not just how we do business\u2014it is our business. \u00A0Do you dare to look at the world's challenges and see impactful possibilities? \u00A0Do you want to contribute to making a better future? \u00A0If the answer is yes, we invite you to consider joining us in boldly challenging what's possible for a sustainable world.

Learn about our benefits designed for you to Thrive at work and at home.\u00A0

We boldly go.

Where is the work:

Monday to Thursday, work onsite with your colleagues. Fridays, choose your work location, balancing what your work requires.

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What's in it for you:
Be a part of our mission!

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As a world leader in creating comfortable, sustainable, and efficient environments, it is our responsibility to put the planet first. For us at Trane Technologies, sustainability is not just how we do business\u2014it is our business. If you want to apply advanced AI to high-impact engineering systems and help define the future of physics-based product development, we invite you to join us.

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What you will do:

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Develop and apply AI/ML methods for CFD and multi-physics simulation problems, especially in fluid flow, heat transfer, turbulence, and system-level thermal management.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Build physics-informed neural networks and related scientific ML approaches for forward modeling, inverse problems, parameter estimation, data assimilation, and hybrid simulation workflows.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Create surrogate and reduced-order models that accelerate high-fidelity simulation while preserving engineering accuracy.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Apply Design of Experiments methods to simulation campaigns, data generation strategies, sensitivity analysis, and efficient exploration of high-dimensional design spaces.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Work with structured and unstructured simulation data from commercial and open-source solvers such as ANSYS Fluent, STAR-CCM+, OpenFOAM, Moldflow, or similar platforms.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Design training, validation, and benchmarking workflows for scientific ML models using both simulated and experimental datasets.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Partner with domain experts, software engineers, and product teams to deploy research into usable tools and scalable engineering workflows.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Partner with external vendors and strategic technology providers to evaluate, adapt, and transition advanced AI/ML solutions into scalable in-house capabilities and engineering workflows.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Contribute to technical strategy in physics AI, scientific machine learning, model validation, and engineering optimization.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Communicating results clearly to technical and non-technical stakeholders and support adoption across the organization.

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What you will bring:

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 PhD or MTech in Mechanical Engineering, Aerospace Engineering, Applied Mathematics, Computer Science, Physics, or a closely related field with strong emphasis on CFD or computational science.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Strong foundation in computational fluid dynamics, numerical methods for PDEs, turbulence modeling, and heat transfer.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Demonstrated experience applying physics-informed neural networks or related methods to engineering or scientific computing problems.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Strong programming skills in Python and experience with scientific ML frameworks such as PyTorch, TensorFlow, or JAX.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Experience building end-to-end ML workflows including data preparation, training, evaluation, hyperparameter tuning, and model deployment.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Experience working in Linux and HPC environments, including parallel computing and GPU-based training (desirable).

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Strong understanding of model verification, validation, uncertainty, and physical consistency in engineering applications.

\u00B7\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0\u00A0 Excellent communication and collaboration skills with the ability to work across research and engineering teams.

We offer competitive compensation and comprehensive benefits and programs. We are an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, pregnancy, age, marital status, disability, status as a protected veteran, or any legally protected status.