At Flowstate, we are harnessing AI and machine learning for advanced pipeline leak detection. Our mission is to develop innovative solutions for challenging problems in the detection of leaks in complex fluids and systems. We combine cutting-edge technology with practical applications, aiming to make a significant impact in the industry.
POSITION SUMMARY
We are seeking a Computation Fluid Dynamics Engineer to become a key member of our R&D team at Flowstate. This role is centered around providing expert knowledge in hydraulic engineering and computational modeling, working alongside our data scientists to integrate physics principles into machine learning (ML) models for enhanced pipeline leak detection. The ideal candidate will be the cornerstone for our engineering and computational efforts, focusing on fluid dynamics, hydraulic modeling, and contributing to the integration of these models with data-driven analyses.
KEY RESPONSIBILITIES
- Lead the development and optimization of hydraulic and computational models, focusing on fluid dynamics in pipeline systems.
- Collaborate closely with data science teams to integrate physics-based insights into ML models and analyses.
- Provide expert input on hydraulic engineering aspects for the enhancement of ML-driven leak detection capabilities.
- Contribute to the refinement of simulation and modeling strategies.
- Drive initiatives for the application of computational models in understanding and solving complex fluid dynamics problems.
- Guide the team in selecting and applying the most appropriate physical models and computational methods for diverse scenarios in leak detection.
- Support the validation and refinement of models, ensuring their compatibility and effectiveness with data science methodologies.
JOB SKILLS AND QUALIFICATIONS
Required
- Advanced degree in Engineering, Physics, or a related field with a strong focus on fluid dynamics or computational physics.
- Extensive experience with CFD software (e.g., ANSYS Fluent, OpenFOAM) in setting up, running, and analyzing simulations.
- Solid understanding of fluid mechanics, thermodynamics, and related physical principles.
- Experience in high-performance computing (HPC) for large-scale simulation projects.
- Proficiency in numerical methods and algorithms pertinent to fluid dynamics simulations.Strong analytical skills for interpreting simulation results and deriving insights.
- Proven ability to adapt to changing priorities, needs, and deadlines.
- Excellent time management skills and ability to deliver results with minimal oversight.
- Strong collaborative skills, with a history of working effectively in team environments.
- Must have US residency; no visa support available.
Preferred
- Background in multi-phase flow modeling and turbulence modeling techniques.
- Experience with Python, particularly its scientific computing libraries (e.g., NumPy, SciPy) and machine learning frameworks (e.g., TensorFlow, PyTorch).
- Background in network analysis, machine learning, data science, or related fields.
- Proven track record of integrating physics-based models with data-driven solutions.
- Ability to translate complex engineering and computational concepts for collaborative projects with data science teams.
- Deep knowledge of mesh generation techniques and mesh independence studies
WHY YOU SHOULD JOIN US
Joining Flowstate means becoming part of a team dedicated to tackling some of the most challenging and impactful problems in pipeline safety using AI and machine learning. We value innovation, collaboration, and the ability to translate complex data into meaningful solutions. Your contributions will be pivotal in advancing our mission and making a real difference in an industry critical to our environment and society.
We are not currently sponsoring visas for this position. References may be requested. Applicants not having the required skills may not receive a response.