A major aim of the Research Campus MODAL is the development and use of mathematical synergies between the individual labs of the network. In this context, MODAL SynLab, the Synergy Laboratory, aims to generalize problem-specific solution algorithms developed in each laboratory and implement it in a structure-specific way. At the core of these activities is the development of the SCIP Optimization Suite, a software package for solving constraint and mixed-integer nonlinear optimization problems to global optimality. Learning from the problem-specific successes in the application-specific labs, our main goal is to accelerate the existing solution algorithms and extend the class of problems that can be handled. This way we create an optimization tool that can form a stronger basis for future research projects.
Satalia is an optimization solutions company. Based in London, they develop SolveEngine, a platform that aims to make optimization technologies more accessible to practitioners. The company also produces stand-alone optimization tools, which it uses in some of its consulting projects for customers worldwide. Across the company, they use a diverse set of exact and heuristic algorithms such as satisfiability solving, mixed-integer linear programming, and constraint programming, both in general and problem-specific implementations. It is their declared mission to transfer cutting-edge optimization technology developed in academia to practice. The G-RIPS project will be accompanied by their Berlin-based consultants.
Satalia's SolveEngine interfaces to a variety of optimization algorithms. A given optimization problem can often be solved by many of these algorithms, but their performance can vary widely in practice. Predicting the best-performing solver on-the-fly and under limited response time is an unsolved question. The aim of this project is to investigate and compare different machine learning techniques in order to select well-performing algorithms from instance features that can be collected with limited computational effort. This will involve understanding various machine learning algorithms, the design of experiments, and the use of existing machine learning packages and own implementations. Practitioners from Satalia and mixed-integer programming solver developers from MODAL SynLab will provide support. Students will gain experience in the implementation and use of machine learning as a research tool and gain insight into the world of mixed-integer programming algorithms.
We welcome applications from highly motivated team-players who ideally
- have a background in mathematical optimization, computer science, and/or machine learning,
- have experience in working in a Linux/Unix environment and collaborative work on source code (e.g. working with revision control systems),
- have experience with the scripting language Python and a high-level programming language (e.g. C++),
- are fluent in English (written and spoken),
- and have a general curiosity to learn new research skills along the way.