Post-Doctoral Research Visit F/M Graybox Tunneling for Multi-Objective Combinatorial Optimization: Algorithmic and HPC Perspectives

id="offer-description" class="ecl-u-type-heading-2 ecl-u-mt-none ecl-u-mb-l">Offer Description The postdoctoral researcher will contribute to the design, development, and experimental validation of novel graybox tunneling algorithms for multi-objective combinatorial optimization , with a particular focus on their parallelization and scalability on high-performance computing (HPC) platforms. The main assignment will be to investigate how knowledge about the structure and local interactions of combinatorial optimization problems can be exploited to design efficient tunneling mechanisms capable of navigating large sets of Pareto local optima . In particular, the researcher will contribute to extending existing single-objective tunneling principles to the multi-objective setting and to developing new algorithmic strategies for efficiently exploring different regions of the Pareto front. A second assignment will concern the identification and exploitation of different levels of parallelism arising within tunneling-based multi-objective search . This can include solution-level and iteration-level parallelism, as well as higher-level parallelism based on concurrent tunneling processes. For exemple, the researcher will investigate hybrid optimization strategies, notably based on distributed island models, in which different tunneling processes explore complementary regions of the search space and exchange selected Pareto local optimal solutions. The researcher will also contribute to