Phd Position artificial Intelligence for Improved

vor 2 Wochen

Graz, Österreich MathHire.org Vollzeit

The Combinatorial Optimization group within the Institute of Discrete Mathematics at Graz University of Technology is seeking a PhD student to work on an applied optimization project aiming at efficient solutions for real-world container loading problems as a contribution to a greener future.

**Context of position/Advisors**

A** fully funded PhD position** is offered for **3 years**. The starting date is **September 1, 2024** (slightly later or earlier starting dates are possible upon mutual consent).

The annual gross salary is 50103.2 Euro (employment for 40 hours per week, no teaching obligations involved). The position is part of the research project** "Artificial Intelligence for Improved Container Loading Efficiency"** (AI4CL) which is funded by the **Austrian Research Promotion Agency (FFG) **within

is offering software-as-a-service solutions for holistic supply chain optimization. The PhD student will be supervised by_ Eranda Dragoti-Cela_ and _Bettina Klinz_
on the side of Graz University of Technology and by _Stefan Lendl_ on the side of S2data.

**Goals/PhD topic**

The goal of the project is to develop new algorithms for container loading problems. The task there is to find an efficient placement of boxes into
transport containers such that special loading constraints (e.g., box stacking rules, weight distribution rules, rules about the placement location
of dangerous goods) are satisfied. A special focus within this project will be on achieving more efficient and greener transports.

From a methodological point of view, the project will involve approaches from different areas of mathematical optimization, computer science and operations
research. The developed suite of algorithms will vary strongly in their mathematical foundation. Techniques that we envisage to make use of include
mixed-integer programming, column generation, local search algorithms and metaheuristics, tree search based algorithms with neural network state-evaluations and constraint generation approaches. The developed algorithms will be tested and evaluated not only on existing academic benchmark data but also on real industry data.

**Requirements**:
Due to the applied character of the project some experience in coding and an interest into optimization problems are an absolute requirement. A solid background in optimization, prior experience with C/C++ or Python, knowledge of MIP solvers and familiarity with machine learning are a plus.

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