Development Scientist

Key Responsibilities

• Performing numerical simulations using in-house coding and Finite Element /Difference
• Analysis software for designing new manufacturing processes and equipment, or for trouble-shooting existing process upsets.
• Develop new models or help other engineers develop models if the existing models are insufficient.
• Apply physical principles to explain data and identify gaps for improving quality or reducing cost. Generate hypothesis to explain the existing gaps in knowledge. Use models or physical tests to confirm or deny hypothesis
• Collaborate with subject matter experts, division engineers and scientists to facilitate and collect data from plants to develop, calibrate and validate math models

Experiences/Education - Required

Education:
Ph.D. in Mechanical Engineering, Chemical Engineering, Civil Engineering, Engineering Mechanics, Geophysics, Computational Science, or Physics or Masters in above with 3-5 years of experience

Required Skills:

1. Strong analytical skills for problem solving
2. Familiar with computational techniques, used in heat transfer, combustion, chemical species, turbulent air flow with species
3. Having a background in tackling complicated problems, defining the underlying physics using fundamental principles and simplifying the problem as much as needed, identifying solution paths, developing solution techniques, and delivering following timeliness

Experiences - Desired

1. Familiarity with commercial software such as Fluent/ANSYS, Star CCM+, MATLAB, COMSOL, AUTO CAD, Solid Works, Design Modeler, Gambit/Cubit
2. Familiarity with Linux, C++, Python
3. Familiarity with coupled heat transfer (conduction-convection-radiation)-fluid flow problems. Familiarity with problems related to radiation, surface-to-surface and participating media with discrete oordinates method, Rosseland approximation. Familiarity with turbulent flow in furnaces, burner design and/or analysis.
4. Confidence in dissecting numerical codes, developing scripts, and contributing to in-house code development.
5. Confidence in large data management to applying in-depth data analysis and statistical tools to the design of experiments and the interpretation of result, and defect identification and categorization.
6. Experience with designing experiments or interacting with experimentalists needed for model validation or development.