MDO Strategy & Leadership: Lead and define Multi-Disciplinary Optimization (MDO) strategies across vehicle development programs, integrating multiple CAE domains including Durability, NVH, Crash/Safety, and CFD to deliver balanced, mass-efficient, and performance-optimized designs
Optimization Framework Development: Architect, build, and manage robust optimization workflows and automation pipelines using tools such as modeFRONTIER, Isight, HEEDS, Optimus, or equivalent MDO platforms. Ensure seamless integration of solver runs, DOE studies, and response surface models within the workflow
Design of Experiments (DOE): Independently plan and execute DOE studies (Full Factorial, Latin Hypercube Sampling, Taguchi, Central Composite Design, etc.) to efficiently explore design spaces across multiple disciplines simultaneously. Interpret results to identify critical design variables and their sensitivities
Surrogate Modeling & Metamodeling: Develop and validate high-fidelity surrogate models / Response Surface Models (RSM) using techniques such as:
Apply surrogate models to reduce computational cost while maintaining accuracy in multi-disciplinary optimization studies
Multi-Objective Optimization Execution: Formulate and solve complex multi-objective optimization problems involving conflicting performance targets across disciplines. Generate and interpret Pareto Front solutions; facilitate engineering trade-off decisions with program and design teams
Topology & Shape Optimization: Lead component and system-level topology optimization studies (density-based SIMP method) using tools like OptiStruct, TOSCA, or equivalent. Translate concept results into manufacturable design proposals in collaboration with product design teams
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Parametric & Morphing Studies: Build parametric models and utilize mesh morphing techniques (HyperMorph, ANSA Morphing, STAR-CCM+ morphing) to efficiently explore geometric design spaces across disciplines without full re-meshing
Supplier Collaboration: Engage with Tier-1 suppliers and external simulation partners on shared MDO tasks; define input requirements, review deliverables, and ensure technical closure of open items
Mentoring & Capability Building: Guide junior and mid-level engineers on MDO methodologies, DOE best practices, surrogate modeling techniques, and automation workflows. Develop internal training material and process documentation to institutionalize MDO capabilities within the organization
Innovation & Benchmarking: Continuously scout and evaluate emerging MDO methodologies, AI/ML-driven optimization techniques, and industry best practices. Propose and pilot new approaches to enhance the efficiency and accuracy of the optimization process
Technical Competencies
a. Expert knowledge of optimization algorithms: Genetic Algorithms (NSGA-II), Simulated Annealing, Gradient-based methods, Particle Swarm Optimization
b. Deep understanding of statistics and probability as applied to engineering DOE and uncertainty quantification
c. Strong fundamentals in Finite Element Analysis, Computational Fluid Dynamics, and structural dynamics
d. Solid understanding of vehicle architecture, lightweight design principles, and manufacturing constraints
e. Familiarity with DFMEA, DVP, APQP processes in an automotive product development context
f. Understanding of Robust Design Optimization (RDO) and Reliability-Based Design Optimization (RBDO) methodologies
g. Knowledge of AI/ML techniques (regression, classification, neural networks) applied to engineering surrogate modeling is a plus
CAE Tool Proficiency:
Expert-level proficiency in HyperWorks suite (HyperMesh, OptiStruct, HyperView), ANSA Working knowledge of additional solvers such as Nastran, ABAQUS, or equivalent is advantageous. Proficiency in post-processing and result interpretation.
