Working Student (f/m/d) - AI-Enhanced Optimization & GPU Computing

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What you bring (please specify briefly but as detailed as possible)Currently enrolled in Computer Science, Mathematics, Physics, or Operations ResearchStrong programming skills in Python and working knowledge of at least one optimization framework (Gurobi, CPLEX, Xpress, or OR-Tools) — requiredSolid foundations in at least one of: mathematical optimization (LP/MIP, heuristics, metaheuristics), machine learning, or algorithm designAbility to design and run reproducible experiments, analyse results rigorously, and document findings clearlyInterest in applied, enterprise-scale problems — not purely academic settingsNice to have: experience with GPU programming (CUDA, cuOpt), ML frameworks (PyTorch, scikit-learn), C++, or operations research decomposition techniques.What you'll buildYou will work on the algorithmic core of the SCM Optimization Service — the mathematical engine behind SAP's supply chain planning. The focus is on three areas:Classical Optimization. You will develop and improve algorithms for core supply chain and logistics problems. The work covers mathematical programming (MIP), heuristics, and metaheuristics, applied to real-world planning scenarios at enterprise scale.AI-Enhanced Optimization. You will develop and evaluate machine learning techniques that augment classical optimization: learned heuristics and prediction models for planning parameters. The goal is to improve planning quality and responsiveness beyond what classical solvers alone can achieve.GPU-A

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