We are now seeking to fill an academic position at Lecturer/Senior Lecturer/Reader rank for a successful academic to join the Division of Systems, Power and Energy Engineering. We specifically seek a candidate who can potentially contribute to the James Watt School of Engineering in the area of modelling of materials and/or processes at lower length scales where continuum assumption ceases to be valid. The position offers a unique opportunity to provide valuable new insights into the integration of advanced manufacturing, including additive and digital manufacturing, with structure – processing – property relations through multiscale modelling, to realize improved processes and new products. For more information about this position, please see the advertisement.

The candidate will work in a project related with smart manufacturing of composite materials by injection/infusion techniques. The project will consist in a joint collaboration between Data Mining Research Group from Universidad Politécnica de Madrid (UPM) and IMDEA Materials Institute. The research activities will be focussed on the application of simulation techniques (computational fluid mechanics) to the optimization of manufacturing process of composite materials by liquid moulding, including the creation of a physical demonstrator. The optimization aims to be carried out by applying Big Data techniques in the context of storage and management and machine learning approaches for the development of predictive models.

More information here: http://jobs.materials.imdea.org/offer/29

The candidate will work in a project related with smart manufacturing of composite materials by injection/infusion techniques. The project will consist in a joint collaboration between Data Mining Research Group from Universidad Politécnica de Madrid (UPM) and IMDEA Materials Institute.The research activities will be focussed on the application of simulation techniques (computational fluid mechanics) to the optimization of manufacturing process of composite materials by liquid moulding, including the creation of a physical demonstrator. The optimization aims to be carried out by applying Big Data techniques in the context of storage and management and machine learning approaches for the development of predictive models.

More information here: http://jobs.materials.imdea.org/offer/29