Data Science + Combinatorial Optimisation
Project description
At the VUB Data Lab we do research on state-of-the-art techniques that combine machine learning methods, including deep learning, with combinatorial optimisation techniques from Operations Research, Mixed Integer Programming and Constraint programming. The goal is to learn part of the problem specification, prices, demand, load, user preferences, etc from historic data, and to do the combinatorial optimisation over the learned models. This can further be improved by doing the learning using decision-focussed learning and other hybrid prediction + optimisation techniques.
The VUB data lab invites interested candidates with expertise in constraint programming, combinatorial optimisation and mixed integer programming on one hand, and machine learning, preference learning, deep learning or causal inference on the other hand, ideally a combination thereof.
About the research Group
Mobility, Logistics and Automotive Technology Research Centre
MOBI is the research leader in electromobility, socio-economic evaluations for sustainable mobility and logistics. With our multidisciplinary team, we support and study the transition towards a more sustainable urban mobility and logistics system with the goal to achieve concrete and long-lasting positive socio-economic and environmental impacts. Our strength resides from our unique combination of environmental, socio-economic and technical competences, together with tools developed for the sustainable transport sector. Our research focuses on five domains: electric & autonomous vehicle technology, battery innovation, sustainable logistics, urban mobility and sustainable energy communities.