PhD student in Geometric Deep Learning

At the department of Electrical Engineering research and education are performed in the areas of Communication and Antenna systems, Systems and Control, Computer vision, Signal processing and Biomedical engineering, and Electric Power Engineering. Our knowledge is of use everywhere where there is advanced technology with integrated electronics. We work with challenges for a sustainable future in society of today, for example in the growing demands concerning efficient systems for communications and electrifying.

We offer a dynamic and international work environment with about 200 employees from more than 20 countries, and with extensive national and international research collaborations with academia, industry and society.

The department provides about 100 courses, of which most are included in the Master’s Programs ”Biomedical Engineering”, “Electric Power Engineering”, ”Systems, Control and Mechatronics” and ”Communication Engineering”.

Read more at www.chalmers.se/en/departments/e2


Information about the research group

The Computer Vision Group conducts research in the field of automatic image interpretation and perceptual scene understanding. The group targets both medical applications, such as the development of new and more effective methods and systems for analysis, support and diagnostics, as well as general computer vision applications including autonomously guided vehicles (particularly self-driving cars), image-based localization, structure-from-motion and object recognition. The main research problems include mathematical theory, algorithms and machine learning (deep learning) for inverse problems in artifical intelligence.


Information about the project

We are interested in widening the scope of Deep Learning, which has proven to be a powerful tool for
many problems in artifical intelligence, in particular, the subfields computer vision, natural language processing, and audio analysis. Geometric Deep Learning, that is, going beyond Euclidean data, has been identified as a major challenge. The proposed project is based on two approaches for Geometric Deep Learning that are able to learn in a more effective way using less annotated data.

The first one is concerned with the internal network structure and the aim is to inject geometric properties such as invariances and priors into the internal representation by construction. The second one uses the fact that often unlabeled data is abundant and correlated, for instance, through time series measurements and consequently the network output should be correlated as well. This can be turned into a supervisory signal for training a network in a weakly supervised setting.

The project is a collaborative project with Prof. Michael Felsberg and the Computer Vision Lab at Linköping University. There will be a companion PhD student in the project placed at Linköping University.

Funding has been obtained from the Wallenberg AI, Autonomous Systems and Software Program (WASP) which is Sweden’s largest ever individual research program, a major national initiative for strategically basic research, education and faculty recruitment. The program is initiated and generously funded by the Knut and Alice Wallenberg Foundation (KAW) with 2.6 billion SEK. In addition to this, the program receives support from collaborating industry and from participating universities to form a total budget of 3.5 billion SEK. Major goals are more than 50 new professors and more than 300 new PhDs within AI, Autonomous Systems and Software. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry. For more information about the research and other activities conducted within WASP please visit: http://wasp-sweden.org/


Major responsibilities

Your major responsibilities are to pursue your own doctoral studies. You are expected to develop your own scientific concepts and communicate the results of your research verbally and in writing, both in Swedish and in English. The position generally also includes teaching on Chalmers' undergraduate level or performing other duties corresponding to 20 per cent of working hours.


Position summary

Full-time temporary employment. The position is limited to a maximum of five years.


Qualifications

To qualify as a PhD student, you must have a master's level degree corresponding to at least 240 higher education credits in a relevant field (physics, mathematics or computer science).

The position requires sound verbal and written communication skills in Swedish and English. If Swedish is not your native language, you should be able to teach in Swedish after two years. Chalmers offers Swedish courses.

Chalmers continuously strives to be an attractive employer. Equality and diversity are substantial foundations in all activities at Chalmers.


Our offer to you

Chalmers offers a cultivating and inspiring working environment in the dynamic city of Gothenburg
Read more about working at Chalmers and our benefits for employees.


Application procedure

The application should be marked with Ref 20180503 and written in English. The application should be sent electronically and be attached as pdf-files, as below:

CV: (Please name the document: CV, Family name, Ref. number)

  • CV
  • Other, for example previous employments or leadership qualifications and positions of trust.
  • Two references that we can contact.

Personal letter: (Please name the document as: Personal letter, Family name, Ref. number)

  • 1-3 pages where you introduce yourself and present your qualifications.
  • Previous research fields and main research results.
  • Future goals and research focus. Are there any specific projects and research issues you are primarily interested in?

Other documents:

  • Copies of bachelor and/or master’s thesis.
  • Attested copies and transcripts of completed education, grades and other certificates, eg. TOEFL test results.

Please use the button at the foot of the page to reach the application form. The files may be compressed (zipped).

Application deadline: 21 October, 2018

For questions, please contact:
Professor Fredrik Kahl, fredrik.kahl@chalmers.se +46 31-772 5057

*** Chalmers declines to consider all offers of further announcement publishing or other types of support for the recruiting process in connection with this position. *** 

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