[DMANET] PhD position in hybrid optimization algorithms for modern supercomputers

The Parallel computing and Optimisation Group (PCOG) at the University of Luxembourg is looking for a PhD candidate in hybrid optimization algorithms for modern supercomputers.

This is a fully funded position for 3 years (extendable to an additional 4th year) in the UltraBO (Ultra-scale Computing for solving Big Optimization Problems) project conducted in collaboration with the BONUS (Big Optimization and Ultra-Scale computing) team of Prof. Melab at the University of Lille (France).
The UltraBO project aims to design and implement novel parallel hybrid optimization algorithms for modern supercomputers. Many experiments reported in the literature demonstrate that high-quality results are obtained through hybridization. However, finding efficient and effective hybridization schemes is challenging and tedious. On the other hand, according to Top500 modern supercomputers are increasingly large (millions of cores), heterogeneous (various CPUs, GPU, FPGA, …) and less and less reliable (Mean Time Between Failures - MTBF<1h) making their programming increasingly complex. The design and implementation of parallel algorithms for these ultra-scale supercomputers is still in its infancy especially in combinatorial optimization.

In this frame, the PhD thesis will tackle the challenge of designing and implementing novel parallel hybrid optimization algorithms (e.g., combining B&B and (meta-)heuristics), considering state-of-the-art approaches such as hyper-heuristics (combining machine learning and optimization).


In Short
• Contract Type: Fixed Term Contract 36 Month (with a further possible 12-month extension if required)
• Work Hours: Full Time 40.0 Hours per Week
• Employee and student status
• Location: Belval, Luxembourg
• Job Reference: UOL05466

The yearly gross salary for every PhD at the UL is EUR 38028 (full time)

More details and application procedure are available here<https://recruitment.uni.lu/en/details.html?id=QMUFK026203F3VBQB7V7VV4S8&nPostingID=80120&nPostingTargetID=118071&mask=karriereseiten&lg=UK>.
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