Dieses Stellenangebot ist nicht mehr verfügbar
Über
Summary
Key Responsibilities
• Formulate complex optimization problems (nonlinear, nonconvex, stochastic, constrained, multi-objective).
• Build advanced optimization pipelines using:
oSciPy Optimize, PySwarms, mystic, pymoo, Bayesian Optimization.
• Develop custom, complex solvers and hybrid algorithms leveraging open-source optimization frameworks.
• Implement objective functions, constraint models, surrogate models, and penalty formulations.
• Integrate optimization techniques into ML workflows (hyperparameter tuning, black-box optimization, surrogate modeling).
• Conduct convergence, sensitivity, robustness, and stability analysis of optimization methods.
• Scale optimization systems using Python, distributed computing, and numerical acceleration.
• Communicate complex mathematical concepts to cross-functional audiences.
Required Qualifications
• Masters/PhD in Operations Research, Applied Mathematics, Computer Science, Engineering, or related quantitative field.
• Expertise in nonlinear, global, evolutionary, and multi-objective optimization (e.g., NSGA-II/III, CMA-ES, DE).
• Strong knowledge of Bayesian Optimization and Gaussian Process modeling.
• Deep mathematical foundation (numerical methods, probability, linear algebra).
• Proficiency in the Python scientific ecosystem (NumPy, SciPy, pandas, scikit-learn).
• Demonstrated ability to design custom solvers for high-dimensional, ambiguous, or poorly behaved optimization landscapes.
Sprachkenntnisse
- English
Hinweis für Nutzer
Dieses Stellenangebot wurde von einem unserer Partner veröffentlicht. Sie können das Originalangebot einsehen hier.