PhD Researcher (M/F/X) Mathematics department > SMAC Unit Project: POSEIDON-NG M/W/X

PROJECT: You will work within the SMAC Unit (Simulation, Modelling, and Analysis of Complex Systems) of the department of Mathematics of the RMA and in close collaboration with the Belgian and Dutch Navies. You will conduct scientific research at university level, leading to a PhD degree, on a project entitled POSEIDON-NG. The project aims to develop a next-generation, uncertainty-aware scheduling capability (computer implementation) to assess, plan, and optimise Mine Countermeasure (MCM) missions carried out by unmanned systems. Responding to Belgium's transition to standoff, drone-centric MCM operations under the Belgian-Dutch rMCM programme, the computer implementation must coordinate multiple unmanned surface, underwater, and aerial vehicles simultaneously, quantify and manage mission uncertainty (e.g., variable detection rates, timing variance, weather windows), generate efficient task sequences and routes, and adapt plans quickly as situational data evolves. The new POSEIDON-NG engine will serve as an advanced decision-support tool for the Belgian and Dutch Navies, providing reliable, conflict-free, risk-informed mission plans, rapid replanning, and operator-oriented features such as what-if (sensitivity) analysis and clear plan-quality indicators. The resulting capabilities will improve the planning, safety, tempo, and overall effectiveness of MCM operations and support the introduction of the new autonomous MCM fleet. This work builds on prior RMA research funded by Belgian Defence. The DAP/22-14 project (SBOS for MCM) delivered the POSEIDON prototype, a two-level mission-planning engine: a low-level module computes detailed vehicle trajectories, sensor-informed detection performance, and the corresponding execution times, while a high-level module uses this information to schedule tasks, allocate vehicles, and coordinate routes across the entire mission using combinatorial optimisation based on Mixed-Integer Linear Programming (MILP). The prototype also established graph-based shortest-path computation between mine zones, automated generation of mine-threat areas, and a practical operator interface to configure mission parameters and visualise plans. The proposed research will extend this foundation with uncertainty quantification, a bidirectional planning loop enabling rapid replanning, scalable optimisation methods (decomposition, heuristics, hybrid solvers), and exploratory quantum(-inspired) optimisation, transforming a promising prototype into an uncertainty-aware, scalable, and deployment-ready operational tool. MAIN TASKS: Extension of the existing MILP-based scheduling formulation with uncertainty-aware constructs (robust, stochastic, or chance-constrained formulations). Development of a bidirectional coupling between the low-level trajectory and risk models and the high-level scheduler, enabling rapid mission replanning. Improvement of the scalability of the scheduling engine through MILP decomposition, heuristics and metaheuris

In the framework of the study POSEIDON-NG (Planned Operations and Scheduling Engine for Integrated Demining in Oceanic Navigation - Next Generation), we are looking for a full-time PhD researcher in mathematical optimisation with a Master's degree in Engineering Science, Applied Sciences, Physics, Computer Science or Mathematics. The Royal Military Academy (RMA) is a military institution and is fully recognized as a university, fulfilling the same criteria as civilian universities. The RMA is also conducting scientific research at university level for projects funded by the Belgian Defense department or external sources. We value diversity and equal opportunities. We firmly believe that diversity enriches our community, and we encourage all qualified candidates to apply.