The Centre for Precision Engineering, Materials and Manufacturing at the Institute of Technology Sligo is looking to recruit a PhD researcher for a programme of work that will focus on controlling and optimising manufacturing processes through the development of advanced methods and tools for modelling, simulating and forecasting the behaviour of holistic manufacturing systems from data sets collected from the factory life cycle phases of industrial collaborators.

Virtually all modern manufacturing systems and processes are characterised by multivariate data However, due to the complex, interrelationship structure of this data and time varying element, it remains a challenge on how best to use this data in decision-making, controlling and optimisation of manufacturing processes.

Statistical Process Control (SPC) is a methodology for providing online control for manufacturing processes and ensuring that any detrimental change in a process is speedily identified and corrected. Traditional SPC, involving univariate data, has been successfully used in steady-state manufacturing processes, but these approaches are no longer valid for use in dynamic behavior environments involving highly autocorrelated processes. Multivariate SPC techniques aims to readdress the issue of controlling modern, dynamic manufacturing processes and represents one of the most rapidly developing areas of statistical process control.

The specific aims of the research programme are:

• Advance sustainable manufacturing knowledge to the development of new methods and tools for modelling, simulation and forecasting the behaviour of holistic manufacturing systems.
• Develop process monitoring and adjustment methodologies for addressing dynamic behaviour problems so that system performance can be controlled and optimised.
• Create stochastic simulation models of the manufacturing system for prediction of future performance and assessment of the relationships between product output and process input parameters.

Objectives of the research programme are:

• Investigate multivariate statistical analysis techniques for analysing and interpreting manufacturing data. This investigation will concentrate on the literature review, manufacturing case studies, application of statistical methodologies and review of appropriate statistical software.
• Investigate, apply and enhance statistical methodologies that enable the forecasting of defect rates and process performance based on process input parameters.
• Develop multivariate Statistical Process Control (SPC) techniques applicable for high yield, dynamic manufacturing processes.
• Develop a simulation model to enable the optimisation of the manufacturing process by incorporating the stochastic uncertainty inherent in the manufacturing process.
• Validate all developed methodologies with the industrial collaborators.

This project is funded by IT Sligo’s president’s bursary programme and provides for a fellowship for the student. Further information: http://staffweb.itsligo.ie/staff/jdonovan/conditions.pdf

Profile of Ideal Candidate

The researcher selected for this position will have a background in Statistics, Mathematics or Engineering. Key skills for the graduate include:

• A 1^st class or 2.1 Honours degree in from an appropriate discipline with a substantial statistical component. Industry experience of working in the in the field would be a distinct advantage.
• Knowledge of multivariate data analysis and operations research would be desirable including the numerical analysis of complex problems.
• Knowledge of simulation tools and process modelling techniques and methodologies.  
• Understanding of qualitative and quantitative research methodologies.
• Strong knowledge of data management and analysis tools such as MS Excel, Minitab or the R programming language would be a distinct advantage in addition to sound computer programming skills.
• Excellent oral, written and analytical skills are essential.

Interested candidates can send CV to Dr. John Donovan (donovan.john@itsligo.ie) and Dr. David Tormey (dtormey<στο>itsligo.ie).