Postdoctoral Researcher - Optimization with Embedded Machine Learning Surrogates
Tasks
- Analyze trade offs between surrogate model fidelity and optimization tractability
- Apply decomposition methods and scalable solution algorithms
- Communicate results through technical reports publications and presentations
- Design optimization formulations integrating neural networks and surrogate models
- Develop optimization frameworks with embedded machine learning surrogates
- Develop solution algorithms for bilinear and nonconvex formulations
- Implement hybrid optimization approaches using mathematical programming and gradient based methods
Perks/Benefits
Skills/Tech-stack
Bayesian optimization | Bilinear Optimization | CPLEX | Convex programming | Decomposition | Derivative Free Optimization | Difference of Convex Programming | Gurobi | GurobiML | IPOPT | Integer Programming | Linear Programming | Machine Learning | Mathematical Optimization | Mixed Integer | Mixed-Integer Nonlinear Programming | Mixed-Integer Programming | Neural Networks | Nomad | Nonconvex optimization | Nonlinear Optimization | Nonlinear Programming | OMLT | Operations Research | Python | Regression | SLSQP | Surrogate modeling
Education
Roles
Postdoctoral Researcher | Research Scientist | Researcher | Scientist
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