Computational Modelling Engineer - Discrete Modelling/Process Modeling

Role & responsibilities

Computational Modelling Engineer - Discrete Manufacturing Modelling

Key Requirements & Responsibilities:

1. Develop and deploy Discrete Event Simulation (DES) and optimization models to improve manufacturing performance and operational efficiency.
2. Design and implement Digital Twin solutions for manufacturing lines, enabling real-time decision support and predictive analysis.
3. Perform capacity planning, throughput analysis, line balancing, and bottleneck identification across manufacturing operations.
4. Apply Operations Research techniques including scheduling, resource optimization, and logistics modelling.
5. Build and validate simulation models using actual manufacturing data, time studies, and shop-floor observations.
6. Utilize advanced modelling concepts such as Queueing Theory, Stochastic Processes, and Optimization Algorithms.
7. Drive improvements in manufacturing capacity, resilience, supply reliability, quality, and compliance readiness.
8. Collaborate with cross-functional teams including manufacturing, engineering, operations, data science, and digital transformation teams.
9. Develop simulation and optimization solutions using tools such as ExtendSim, AspenTech, Frontline Solver, AnyLogic, Simul8, Arena, or equivalent platforms.
10. Serve as a subject matter expert and promote simulation-driven decision-making, Digital Twins, and model-based engineering practices across the organization.

Computational Modeling Engineer - Process Modeling

Top 10 Key Requirements & Responsibilities

1. Develop and deploy steady-state and dynamic process simulation models to improve manufacturing processes and support process design decisions.
2. Build Digital Twin solutions for process equipment and manufacturing operations, enabling real-time monitoring and predictive capabilities.
3. Develop and utilize Computational Fluid Dynamics (CFD) and multiphysics models for unit operations such as mixing, heat transfer, mass transfer, filtration, drying, crystallization, and reaction systems.
4. Apply strong fundamentals in Transport Phenomena, Thermodynamics, Reaction Engineering, and Process Simulation to solve complex manufacturing challenges.
5. Support process scale-up, optimization, troubleshooting, and technology transfer activities across manufacturing facilities.
6. Conduct process modeling and optimization studies to enhance capacity, reliability, compliance, quality, and operational excellence.
7. Validate simulation results through laboratory studies, plant data analysis, and empirical testing.
8. Collaborate with process engineers, manufacturing teams, digital engineering teams, and data scientists to deliver data-driven engineering solutions.
9. Utilize industry-standard software such as ANSYS Fluent, COMSOL, Aspen Plus/HYSYS, M-Star CFD, AFT Fathom, and JMP for modeling and simulation.
10. Drive adoption of first-principles modeling, Digital Twins, and advanced simulation technologies across the organization through technical leadership and knowledge sharing.

Qualification criteria:

• PhD with 4+ years of relevant industry/research experience, OR
• Master's degree with 4-15 years of relevant experience depending on seniority level.