Looking for a fulfilling role working to develop the next generation of advanced powder processes? MAPP is recruiting for a number of posts at locations across the country:


Research Associate in Machine Learning for Advanced Manufacturing Systems, University of Sheffield - Department of Automatic Control and Systems Engineering

This is an exciting opportunity to develop the next generation of computational intelligence tools to advance additive manufacturing (AM) processes. It will involve working on two projects - DAM (Developing Design for Additive Manufacturing) and AIRLIFT (Additive IndustRiaLIsation FuTure Technology), led by GKN Aerospace. At Sheffield research across AIRLIFT and DAM is focused on developing the material properties of AM produced components, designing new in-process metrology including thermal imaging and applying machine learning approaches for the closed loop control of AM processing to achieve 'right first time' manufacturing.

The application deadline is 8th April 2018. Visit www.jobs.ac.uk for more information.


Research Associate in Metal Additive Manufacturing, University of Sheffield - Department of Materials Science and Engineering

This is an exciting opportunity to develop the next generation of AM processes and applications. It will involve working on two projects - DAM (Developing Design for Additive Manufacturing) and AIRLIFT (Additive IndustRiaLIsation FuTure Technology), led by GKN Aerospace. At Sheffield research across AIRLIFT and DAM is focused on developing the material properties of AM produced components, designing new in-process metrology including thermal imaging and applying machine learning approaches for the closed loop control of AM processing to achieve 'right first time' manufacturing.

The application deadline is 8th April 2018. Visit www.jobs.ac.uk for more information.

CASE Studentship with the Manufacturing Technology Centre: High Speed Additive Manufacturing - Developing Productivity for Metal Processing
University of Sheffield 


A funded PhD CASE Studentship (UK/EU only) is available for research into next-generation AM processes based on a new method for selective laser melting of metal powders. The approach is based on diode area melting (DAM) using arrays of laser diodes for efficient, scalable, high-speed parallel processing of 3D components. 
The University of Sheffield has an unparalleled range of both additive manufacturing and optoelectronic facilities and expertise, being host to EPSRC Future Manufacturing Hubs in both Manufacture using Advanced Powder Processes (MAPP) and in Future Photonics. The Manufacturing Technology Centre (MTC) is the lead centre for additive manufacturing technology within the High Value Manufacturing Catapult and is home to the National Centre for Additive Manufacturing, providing a unique demonstration “factory” for AM. 
The Studentship will be based within the Faculty of Engineering, across the Department of Electronic & Electrical Engineering and Department of Mechanical Engineering, with a number of training courses and a placement at the MTC supported by an associated travel budget. 
 

The application deadline is April 08, 2018. Visit findaphd.com for more information.

Bayesian Optimization in Additive Manufacturing, School of Mathematics and Statistics, University of Sheffield

This project will develop Bayesian statistical methods for modelling the way in which materials and manufacturing processes interact in AM. These models can then be interrogated using Bayesian optimization techniques to identify values of process settings and material properties that optimize the quality of the resulting item, or that maximize the information gained from experiments. Using a Bayesian statistical approach will enable combining information from different sources e.g. experiments of different kinds, while accounting for the uncertainty involved in a rigorous and coherent manner. 

Full funding is available for up to 3.5 years, including a stipend at RCUK rates  and full fees. This project must start by the end of July 2018. The project is partly funded through a Graduate Teaching Assistantship, and the successful applicant would be expected to undertake teaching in SoMaS (tutorials and marking, and in some cases lecturing) for up to 6 hours a week during the 22 weeks of the teaching year. We will provide appropriate induction and support. In later years these teaching duties could develop in discussion with your supervisors to reflect your abilities.

The application deadline is April 13, 2018. Visit findaphd.com for more information.