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The role is responsible for developing and executing CFDbased Design of Experiments (DOE) to characterize mixing performance in stirred vessels across laboratory, pilot, and commercial scales. It involves building validated CFD models, extracting key hydrodynamic metrics, and generating structured datasets that describe the impact of process and design variables. The position translates simulation results into reducedorder models and digital tools to support process understanding and scaleup decisions. This work enables efficient, datadriven development of robust and scalable mixing processes while reducing experimental and computational effort.
Job Responsibility:
Develop and execute CFDbased Design of Experiments (DOE) to systematically characterize mixing performance in stirred vessels across laboratory, pilot, and commercial scales.
Build highfidelity CFD models that accurately represent vessel geometries, impeller configurations, and operating conditions used in development and manufacturing.
Quantify key hydrodynamic and mixing responses to assess mixing performance.
Generate structured simulation datasets that capture the impact of critical process and design variables on mixing behaviour.
Develop reducedorder models (ROMs) that enable rapid prediction of mixing and hydrodynamic performance without full CFD simulations.
Requirements:
Enrolment in a master’s program in Chemical Engineering, Mechanical Engineering, Process Engineering, or a closely related discipline is required.
A strong interest in fluid dynamics, mixing processes, and transport phenomena relevant to pharmaceutical or chemical engineering is demonstrated.
Foundational knowledge of Computational Fluid Dynamics (CFD), including numerical methods, turbulence modelling concepts, and interpretation of simulation results, is expected.
Experience with or exposure to CFD software tools such as ANSYS Fluent or equivalent is gained through coursework, projects, or internships.
The ability to analyse and interpret simulation data to extract meaningful physical and engineering insights is essential.
Proficiency in data analysis and visualization using tools such as Python, MATLAB, or equivalent programming environments is demonstrated.
Clear communication of technical results in written reports and presentations is required.
Effective collaboration in a multidisciplinary research or engineering environment and a structured, problemsolving mindset are expected.