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PhD in Inverted Neural Networks for Optimizing Permanent Magnet Structures

Technical University of Denmark is delighted to offer a PhD position in artificial intelligence. The funds for this position are available for 3 years. This position is closed on January 14, 2019.

Can inverted neural networks (NN) be used for optimizing permanent magnet structures? Can key properties such as magnetic field homogeneity, field magnitude and magnetic forces be assessed by an NN? If so, we may be able to open up a new area of research where creative artificial intelligence (AI) is used for making novel designs rather than using traditional and tedious optimization algorithms while at the same time clearing the path for designs and solutions hitherto un-thought of by humans.

This will have a huge impact not only on magnet design, but also on any other design problem where simple fundamental building blocks combine to a complex solution. The PhD project will be focused on applying an existing numerical model of magnetostatics for training NNs, using these for predictions and then go for inverting the NNs in order to get novel/creative solutions to permanent magnet optimization challenges. This PhD position at DTU Energy is a part of a unique research project called AiMADE (http://www.aimade.org/) where we exploit artificial intelligence to solve problems related to energy technologies. We will study the use of neural networks for optimizing permanent magnet structures by realizing a novel and fast computational model build on an existing computational framework for magnetic field calculations and written in Fortran. Your role as a PhD student will be to investigate the possibility of training neural networks, or other AI-related approaches, on various magnet configurations modelled with our in-house code. Once trained and the system has reached an acceptable level of recognizing hitherto unseen structures for their key-properties (field magnitude, homogeneity etc.) you will work on inverting the system in order to reach predictive capability. You will be asking the neural network how a magnet configuration should look like when specifying a certain goal. You will be working together with other PhD students that also use this code / framework for other magnetism related problems and will thus from the beginning of the project enter into a group of young people with similar interests and technical challenges. The main activity will take place at the Department of Energy Conversion and Storage at DTU Ris campus initially and from ultimo 2019 at DTU Lyngby campus, as the department is being united at this campus. and must have a two-year master’s degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master’s degree, Candidates with the following qualifications are preferred

The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see the DTU PhD Guide.

DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility.

The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union.

The period of employment is 3 years.

You can read more about career paths at DTU here.

The expected starting date is March 2019 or thereafter.

Please contact Associate Professor Kaspar K. Nielsen, kaki@dtu.dk, Associate Professor Rasmus Bjrk, rabj@dtu.dk or Associate Professor Peter Stanley Jrgensen (psjq@dtu.dk), for further information. Please do not send applications to these e-mail addresses, instead apply online as described below.

Please submit your online application no later than 14 January 2019 (local time).

To apply, please open the link “Apply online”, fill out the online application form.

Summary:

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