Research, design, and validate algorithms for circuit simulation and optimization while collaborating cross-functionally and documenting results.
At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.Core ExpertiseApplied Mathematics (any of the following is a plus)Familiarity with Machine Learning (preferred)Any prior Experience in the following areas is a plusResponsibilities
We seek a graduate researcher-practitioner in applied mathematics/statistics to advance algorithms for electronic circuit simulation, Monte Carlo yield analysis, and optimization. You will work cross-functionally to turn deep math into production-grade technology.
Qualifications- Graduate degree in applied mathematics, statistics, or a closely related field (CS with strong math focus).
- Demonstrated ability to conduct literature reviews, translate theory to practice, and deliver innovative results in real-world settings.
- Statistical inference: significance testing (p-values, confidence intervals), Bayesian statistics, design of experiments, Monte Carlo methods (random sampling, density estimation).
- Rare-event and reliability analysis (a plus): importance sampling, subset simulation, cross-entropy methods, extreme value/tail modeling, yield estimation.
- Surrogate modeling and Uncertainty Quantification (a plus): Gaussian processes, polynomial chaos, sparse grids, variance reduction.
- Optimization: linear, nonlinear, convex, integer, stochastic, variational; robust/multi-objective; derivative-free/global methods (e.g., CMA-ES, Bayesian optimization).
- Numerical analysis: numerical linear algebra (sparse/Krylov/preconditioning), stiff ODE/DAE solvers, approximation, quadrature; model reduction (POD/MOR).
- Differential equations: ODE/PDE/SDE, dynamical systems.
- Probability and statistics: stochastic processes, inference, uncertainty quantification.
- Data science: statistical learning, optimization for ML, dimensionality reduction.
- Classical ML: regression (linear/logistic), regularization (ridge/lasso), classification (SVM, kNN), ensembles (trees, random forests, boosting).
- Contemporary AI (a plus): graph neural networks, transformers, reinforcement/transfer learning, representation learning, active learning.
- Programming proficiency in Python and/or C++ is a plus (NumPy/SciPy, PyTorch/JAX, performance optimization, clean APIs).
- Strong computer science background is a plus (data structures, algorithms, version control, testing, CI/CD).
- HPC/parallel computing (a plus): MPI, CUDA, distributed workflows.
- Scientific computing in one or more areas: computational electromagnetics, fluid/thermal/molecular dynamics, computational physics, or electrical circuit simulation.
- Electronic design automation (EDA): SPICE/Spectre/Verilog-A, netlists, PVT/Monte Carlo flows, yield/parametric corners.
- Research, design, and validate algorithms for circuit simulation, rare-event estimation, and optimization.
- Quantify accuracy/speed vs. baselines; perform rigorous statistical analyses.
- Build robust, maintainable implementations and integrate with production toolchains.
- Good Team Player as well as collaborate with cross-functional teams and document methods and results clearly.
The annual salary range for California is $136,500 to $253,500. You may also be eligible to receive incentive compensation: bonus, equity, and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure. Please note that the salary range is a guideline and compensation may vary based on factors such as qualifications, skill level, competencies and work location. Our benefits programs include: paid vacation and paid holidays, 401(k) plan with employer match, employee stock purchase plan, a variety of medical, dental and vision plan options, and more.
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