PhD scholarship in Physics-informed Deep Learning for Automated Analysis of Energy Materials – DTU Energy

Are you a curious person, passionate about programming, deep learning, and physics? Do you want to develop innovative computational methods that directly support key breakthroughs in the green energy transition? Then we have a great opportunity for you!

We are looking for a highly motivated candidate to join us as a PhD student.

At DTU Energy we offer a friendly working environment built on multidisciplinary collaboration and mutual respect. The department is comprised of world-leading experts across several fields, and our research is supported by state-of-the-art equipment. We offer a high level of autonomy, allowing you to actively shape the project’s direction alongside your supervisors as it progresses.

This PhD is part of the IMITATION (Image interpretation by taught simulation) project that explores the interplay between physics-based simulations and deep learning. X-ray and neutron imaging are becoming invaluable tools for the non-destructive visualization of the internal structure and composition of energy materials, such as electrodes for batteries and electrolysis cells. However, the analysis of the collected data is still largely performed with classical methods that rely heavily on human inputs. These methods are time-consuming and error-prone, limiting the accuracy of the quantifications and predictions of materials’ properties and performance. Deep-learning methods offer new possibilities for improving the data analysis workflow, but their success requires large amounts of training data, not readily available in the field of X-ray and neutron imaging.  

The vision for this PhD project is to use rapid simulations of how X-rays and neutrons interact with materials, to form physically realistic images to be used as the training data for deep learning. Using this approach, we aim to create a flexible and powerful deep-learning framework for quantitative image analysis across different imaging modalities and length scales. 

The project offers the chance to work on deep learning with an immediate application, focusing on solving relevant problems in materials for the green energy transition.

You will be affiliated with the section for Structural Analysis and Modeling (SAM) together with other colleagues working with advanced characterization and simulation techniques.

Responsibilities 

The project exists in the intersection between deep learning, physics, and materials science. Your role as a PhD student will be to:

• Implement, extend, or develop methods for rapid simulation of image generation using X-rays, or neutrons.
• Implement, extend, or develop a suitable deep-learning architecture for image segmentation and analysis.
• Develop a framework in Python and PyTorch that combines physics simulation and deep learning.
• Apply the developed framework to facilitate advanced quantitative analyses in collaboration with energy materials specialists at the department.

At the end of the PhD project, we will have realized an image analysis framework ready to be shared with other scientists and showcased to potential interested industries.

Qualifications

The successful candidate has a MSc degree in a relevant field, computer science, physics, materials science or similar. The candidate should be motivated to specialize in quantitative image analysis of energy materials. The candidate should have:

• Experience with programming.
• Experience with deep-learning frameworks such as PyTorch or TensorFlow is preferred but not required
• Basic knowledge of deep learning concepts as gained from e.g BSc or MSc courses.
• Basic knowledge of X-ray and neutron physics is beneficial but not required.
• Basic knowledge of energy materials science is beneficial but not required.
• Ability to work independently, to plan and carry out complicated tasks.
• Ability to communicate fluently in English both in written and oral form.

You 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.

Approval and Enrolment 

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 DTU’s rules for the PhD education. 

Assessment

The project leader, Associate Prof. Peter Stanley Jørgensen and Head of Section, Prof. Luise Theil Kuhn will together with Assistant Prof. Salvatore De Angelis assess the applicants.

We offer

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.

Salary and appointment terms

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. The position is a full-time position. Starting date is 1 April 2025 (or according to mutual agreement).

You can read more about career paths at DTU here.

Further information

Please contact Associate Prof. Peter Stanley Jørgensen (+45 93511607) if you need further information concerning this position. Please do not send applications by e-mail; instead, apply online as described below.

You can read more about DTU Energy at www.energy.dtu.dk/.

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU. 

Application procedure 

Your complete online application must be submitted no later than 1 December 2024 (23:59 Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link “Apply now”, fill out the online application form, and attach all your materials in English in one PDF file. The file must include:

• A letter motivating the application (cover letter)
• Curriculum vitae
• Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale

You may apply prior to ob­tai­ning your master’s degree but cannot begin before having received it. 

All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.