Scientist I, Plant Simulation and Computational Modeling
Position Summary & Role
Reporting into the Director of Process Modeling and Analytics, the Scientist of Plant Simulation and Computational Modeling will be responsible for building computational models and process simulators to optimize and accelerate bioprocess scale-up and technological transfers across RESILIENCE manufacturing network.
The candidate will develop simulations models and plant simulations that will integrate bioprocess economics, and unit operation mechanistic understanding. These models will be used to understand cost implications of technology and processes as well as help in maximizing the yield and selectivity.
Additionally, the candidate will collaborate with cross-functional experts in the field of data science, operations, manufacturing, and supply chain to validate and deploy models.
Responsibilities will also include evaluation of existing simulation approaches and integration of modeling and simulation techniques like discrete-event based simulation (DES), population-balance modeling (PBM), and discrete element modeling (DEM).
Responsibilities
- Apply chemical & process engineering principles, and first-principle approaches to build predictive and/or prescriptive computational models to enable process optimization and accelerate technology transfer across sites.
- Lead modeling initiatives in process flow sheet simulations including but not limited to heat and material balance analysis, bioprocess economics analysis, discrete event simulations, plant scheduling and optimization, capacity planning, and facility fit across RESILIENCE manufacturing network.
- Develop mathematical and data-driven models to map interactions of biological, chemical and hydrodynamic parameters and develop approaches for the creation of bioprocess "Digital Twins".
- Develop multi-scale hybrid models to couple spatial local hydrodynamic effects with the plant simulation environment to enable optimization of unit-ops design and operations.
- Collaborate with RESILIENCE franchises and stakeholder for the input data requirements, data transfer, development, deployment, and verification and validation of simulation models.
- Effectively communicate modeling results to key stakeholders through project status meetings, presentations, and reports.
- Establish best practices and workflow for model development, pre/post processing, compute resources, and data storage.
Preferred Experience, Education & Qualifications
- Advanced degree in Chemical Engineering, Mechanical Engineering, Physics, Applied Mathematics, Computational Biology, or related engineering fields with a strong background in fluid mechanics, transport phenomenon, and reaction engineering.
- PhD preferred but not required
- Experience with process simulation tools (BioSolve, gPROMS, ASPEN, SuperPro) and/or building computational models with scripting tools like MATLAB, Python or R is highly desirable.
- Experience in discrete event simulations, monte carlo simulations, optimization techniques, data modeling, and/or statistical analysis is a plus.
- Excellent applied experience in using modeling software (ANSYS Fluent, Star-CCM+, and/or OpenFOAM) and solid understanding of Linux and Cloud computing environment.
- Excellent verbal and written communication skills together with ability to handle multiple tasks and adapt to evolving priorities.