Open Positions

Development and design of scheduling system for Robotic Process Automation

We are looking for a motivated and talented student interested in participating in the development of an advanced scheduling system for a Robotic Process Automation (RPA). RPA is a modern technology that automates repetitive business processes using software “robots.” It plays a crucial role in banks, insurance companies, and large institutions, where it helps streamline back-office operations, reduce human error, increase compliance, and significantly cut operational costs. As part of our team, …

Intended for: Students
Workload: Part-Time
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Hybridization of Constraint Programming for Scheduling Problems with Complex Criteria

Today, Large Neighborhood Search (LNS) [PER] is the preferred heuristic algorithm in solvers like CP Optimizer, Google OR-Tools, or OptalCP, as it offers a better ability to escape local optima and higher diversity in the exploration than previously often used Local Search. At the same time, Failure-Directed Search (FDS) [VIL] is used to efficiently explore the whole search space using a Fail-First principle to prove that no solution or better solution than the one found so far exists. …

Intended for: PhD or Postdoc
Workload: Full-Time
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Learning-Augmented Combinatorial Optimization Algorithms for Scheduling and Packing

The project addresses difficult scheduling and packing problems in the sense of computational complexity, for which classical exact approaches are often impractical at realistic scales. The goal is to design new algorithmic frameworks that combine established tools from Operations Research with modern Machine Learning methods to produce high-quality solutions within acceptable computational times. While rooted in OR, the project requires and will further develop strong competencies in Machine …

Intended for: Postdoc
Workload: Full-Time
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Solving Large-Scale Scheduling Problems: Hybridization, Parallelism, and Model Diversity in Constraint Programming

Scheduling problems such as the Resource-Constrained Project Scheduling Problem (RCPSP) remains one of the central challenges in combinatorial optimization, particularly in large-scale industrial settings. As instance sizes grow and objective functions become more sophisticated, classical exact or single-strategy heuristic approaches are no longer sufficient. Future progress requires carefully designed hybrid and parallel solution frameworks. Today, Large Neighborhood Search (LNS) is the …

Intended for: Postdoc
Workload: Full-Time
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