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July 2015

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Subject:
From:
John Bagterp Jørgensen <[log in to unmask]>
Reply To:
John Bagterp Jørgensen <[log in to unmask]>
Date:
Wed, 1 Jul 2015 20:33:23 -0400
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Department of Applied Mathematics and Computer Science at the Technical University of 
Denmark has a vacant PhD position related to Model Predictive Control and injection moulding. 
In the project we will use new sensor technology in combination with Model Predictive Control to 
significantly advance high precision injection moulding manufacturing technology.

The project is a collaboration between the Technical University of Denmark and LEGO and is 
part of the Manufacturing Academy of Denmark, MADE (www.made.dk). In the project you will 
do research combing new sensor technology, model predictive control, and statistical process 
control into a new quality control system for high precision injection moulding. You will test this 
system at experimental facilities at DTU as well as in an industrial plant at LEGO in Billund.

Candidates for the position must have a master degree in mathematical modelling and 
computing, computational science and engineering, physics, electrical engineering, mechanical 
engineering, chemical engineering or equivalent academic qualifications. The candidate must 
document skills within numerical modelling and scientific computing for dynamical systems, 
model predictive control, numerical optimization, statistics and programming. Knowledge of and 
experience with injection molding processes and high precision manufacturing are an 
advantage. Furthermore good communication skills in English as well as strong interpersonal 
and collaboration skills are required.

As part of the PhD project the candidate must be able to work at the DTU campus in Lyngby, at 
the LEGO factory in Billund, and at an international university during an external research stay.

The position will be vacant from November 1, 2015 and the application deadline is September 1, 
2015.

You can read more about the position here
http://www.dtu.dk/Job/job?id=de105f36-17bc-40ac-8fc8-46807d420bc3

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