Senior Engineer / Scientist – CMP Fundamentals, Modeling & Simulation

Qnity Electronics, Inc.; Newark, Northern (DE, KY); Hybrid; USD 110,000 - 170,000; Full time

Job summary

Qnity Electronics, Inc. seeks a Senior Engineer/Scientist to lead CMP fundamentals modeling and simulation efforts. You will build physics-based models, optimize solvers, and validate simulations against experimental data to guide product development.

Responsibilities include leading investigations, collaborating with research, applications, manufacturing, and data science teams; translating scientific insights into design guidance and published work while advancing CMP process understanding.

Qualifications

• Master's or Ph.D. in a relevant field.
• Less than 5 years of industrial experience applying scientific and engineering principles.
• Experience in mathematical modeling of physical systems and experimental method development.
• Strong background in computational modeling, data analysis, and scientific computing.
• Proficiency with MATLAB, Python, Maple, or similar environments.

Responsibilities

• Lead complex investigations requiring deep CMP fundamentals knowledge and scientific problem solving.
• Build physics-based simulation models from first principles and validate them with experiments.
• Collaborate with research, applications, product development, manufacturing, and data science teams.
• Communicate findings through technical reports, presentations, patents, and publications.
• Translate scientific understanding into actionable design guidance for product development.

Skills

Transport phenomena

Fluid mechanics

Polymer mechanics

Contact mechanics

Tribology

Surface characterization

Materials characterization

Multidisciplinary collaboration

Education

Master's or Ph.D. in Mechanical Engineering, Chemical Engineering, Materials Science, Physics, Applied Mathematics, or related discipline

Tools

ANSYS

Abaqus

COMSOL

HPC

Job description

Senior Engineer / Scientist – CMP Fundamentals, Modeling & Simulation

Key Responsibilities:

• Technical Leadership & Scientific Problem Solving
• Lead complex technical investigations requiring deep understanding of CMP fundamentals, material behavior, and process interactions.
• Apply rigorous science-based methodologies to solve short- and long-term technology challenges.
• Partner with research, applications, product development, manufacturing, and data science teams to accelerate innovation.

Simulation Reliability, Validation & Capability Development:

• Build complex, physics-based simulation models from first principles
• Optimize model architecture, meshing strategy, solver settings, and runtime efficiency to enable reliable and practical use in product development.
• Evaluate model assumptions, governing equations, constitutive relationships, material models, and boundary conditions to ensure simulations accurately represent physical behavior.
• Verify model accuracy through numerical checks, experimental validation, calibration, and uncertainty assessment.

Experimental Research & Characterization:

• Design validation experiments in partnership with scientists and engineers.
• Develop methods for model calibration and parameter identification.
• Leverage characterization tools and experimental resources to improve understanding of polishing mechanisms.
• Serve as an internal expert in texture characterization, material behavior, and performance-driving mechanisms.
• Correlate simulation results with laboratory observations and customer outcomes.

CMP Fundamentals Development:

• Build fundamental understanding of interactions between:
  • Pad materials and structure
  • Solid Mechanics
  • Fluid mechanics
  • Contact mechanics
  • Tribology and wear
  • Semiconductor process operating conditions
  • Translate scientific understanding into actionable design guidance and product development recommendations.
• Develop and manage collaborations with internal and external technical experts.
• Communicate findings through technical reports, presentations, customer interactions, patents, and publications.

Required Qualifications:

• Master's degree or Ph.D. in Mechanical Engineering, Chemical Engineering, Materials Science, Physics, Applied Mathematics, or related discipline.
• Less than 5 years of industrial experience applying scientific and engineering principles to solve complex technical problems.
• Demonstrated experience in mathematical modeling of physical systems and experimental method development.
• Strong background in computational modeling, data analysis, and scientific computing.
• Proficiencywith MATLAB, Python, Maple, or similar technical computing environments.
• Expert knowledge inatleastsome ofthe following areas:
  • Transport phenomena
  • Fluid mechanics
  • Polymer mechanics
  • Contact mechanics
  • Fracture mechanics
  • Tribology
  • Surface characterization
  • Materials characterization
  • Comfortable working across theory, simulation, experimentation, and characterization.
  • Strong interpersonal and collaboration skills with multidisciplinary technical teams

Desired Skills:

• Familiarity with CMP consumables, processes, and polishing mechanisms.
• Understanding ofpolyurethane structure-property-performance relationships.
• Experience with:
  • Finite Element Analysis (FEA/FEM)
  • Computational Fluid Dynamics (CFD)
  • Multiphysics Simulation
  • Model Verification & Validation (V&V)
  • Uncertainty Quantification
  • Sensitivity Analysis
  • Hands-on experience with:
  • ANSYS
  • Abaqus
  • COMSOL
  • High PerformanceComputing (HPC)
• Experience evaluating:
  • Model assumptions, governing equations, and constitutive relationships
  • Numerical stability, convergence behavior, and solution robustness
  • Mesh and time-step independence studies
  • Boundary condition selection and sensitivity analyses
  • Model calibration, parameter estimation, and uncertainty quantification
  • Solver performance, computational efficiency, and runtime optimization
  • Verification and validation (V&V) methodologies for predictive engineering models
  • Correlation of simulation predictions with experimental observations and physical behavior