Dr Jules Simo

He supervises postgraduate students on a broad range of Engineering and Computing activity-related issues. Dr Simo is open to PhD applicants with strong backgrounds in Engineering, Computer Science, Applied Mathematics or Physical Science.

Dr Jules Simo's research interests include orbital mechanics, space mission analysis and design, solar sail mission applications, robotics and autonomous systems, dynamical systems theory, Automatic Control. Recent work focused on the guidance and control of space systems as well the application of Multidisciplinary Optimisation and Artificial Intelligence (AI)/Machine Learning (ML) techniques to systems engineering.

In 2007, he was awarded a Marie Curie Fellowship under the European Commission (EC)'s Sixth Framework Programme (FP6). Prior to gaining a research fellow post in the Department of Mechanical and Aerospace Engineering at the University of Strathclyde (UK), he previously worked at the University of Paderborn in Germany, where he still holds a visiting academic position. He has published across a range of Engineering and Computing activity domains. He has undertaken funded research activity across Europe, UK and internationally.

Dr Simo acts regularly as reviewer for scientific journals (e.g., Journal of Guidance, Control, and Dynamics, IEEE Transactions on Aerospace and Electronic Systems, Journal of Spacecraft and Rockets, Advances in Space Research). He also served as Associate Editor, Guest Editor of Special Issues for international academic journals.

He is currently acting as Guest Editor of the Special Issue on "Application of Multidisciplinary Optimisation and Artificial Intelligence Techniques to Aerospace Engineering." The goal of this special issue is to illustrate applications of Multidisciplinary Optimisation (MDO), Machine Learning (ML) and Artificial Intelligence (AI) methods to problems in Aerospace Engineering. Novel ML/AI algorithms and/or application of existing approaches to problems involving space exploration, UAV operations, space robotics, as well as other fields of aerospace engineering will be examined.

With the advancement in AI, robotics and unmanned aerial vehicle (UAV) technology, ML has emerged as a viable technology for solving constrained multi-objective optimisation problems (CMOPs). Aided by advances in hardware and algorithms, modern ML is poised to enable this optimisation, allowing a much broader and integrated perspective.