Project Aims and Objective
The recent world has seen an explosion of available data, gathered from sensors, experiments and direct measurements. The engineering world is shifting from model-centred simulations to the assimilation of data into the modelling process. This opens up a wide area of research where large datasets are used to fine tuning engineering models, design uncertainty sets and infer knowledge about unknown parameters. This is particularly true in optimisation under uncertainties where usually simulation code and an apriori knowledge of the uncertainty distribution of the parameters is known or is assumed to be known. Bringing real data into this process will allow to design robust optimal solutions closer to the real industrial needs and more realistic uncertain scenarios. The main challenges in this field of research want to address the problems of efficiently handling and processing large quantity of data while extracting meaningful knowledge about the optimisation problem and uncertainty sets.
Qualifications
Master degree in computer science, applied mathematics, statistics or aerospace engineering
Experience
Experience in the field of optimisation and/or machine learning techniques is desirable
Student eligibility
UK and EU students, International students will also be considered.
Funding
The selected student will be presented as candidate for the John Anderson Research Award, one of the internal studentship scheme of Strathclyde University. https://www.strath.ac.uk/studywithus/scholarships/strathclyderesearchstudentshipscheme-johnandersonresearchawards/
For any enquires please contact
Annalisa Riccardi, PhD
Lecturer in Computational Intelligence
annalisa.riccardi@strath.ac.uk
__________________________________________
Department of Mechanical & Aerospace Engineering
Strathclyde University
James Weir Building, 75 Montrose Street
G1 1XJ Glasgow
+44.141574.5169
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