Fraunhofer-Gesellschaft

Master's Thesis – Large-Scale Optimization, Supply Chain Logistics

Fraunhofer-Gesellschaft

full-time

Posted on:

Location Type: Office

Location: München • Germany

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Tech Stack

LinuxPythonPyTorchTensorflow

About the role

  • Develop a modular optimization framework for a real-world Inventory Routing Problem
  • Review relevant literature and existing solution approaches
  • Formalize the use case, its scale, constraints, and available data
  • Design and implement a prototypical end-to-end system
  • Research different decomposition strategies and implement a modular solution

Requirements

  • Programming skills, preferably Python, and good software engineering practices
  • Linux, and Git-based version control
  • Knowledge of combinatorial optimization
  • Experience implementing machine learning solutions (in particular reinforcement learning)
  • Experience with one or more of the following is a plus: common optimization libraries (e.g., Gurobi, CPLEX, HiGHS)
  • Machine learning libraries (PyTorch, TensorFlow)
  • Working with real-world data (cleaning, handling missing data, robustness)
  • Enrollment at a German university, preferably in Munich or the surrounding area
  • Particularly suitable for students of M.Sc. Computer Science, Cognition & Intelligence, M.Sc. Data Engineering, M.Sc. Mathematics, and related subjects
Benefits
  • Approachable supervisors
  • Integration into a dynamic team working on innovative tasks
  • Flexible working style (including a workplace at our new institute building in Garching)
  • Insight into applied research practices and collaboration with Fraunhofer
  • Practical relevance for your studies
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
Pythoncombinatorial optimizationmachine learningreinforcement learningGurobiCPLEXHiGHSPyTorchTensorFlowdata cleaning
Soft Skills
software engineering practices