PhD Student in Artificial intelligence and Machine Learning

Chalmers University of Technology is offering PhD position. This fellowship is available for all students around the world. Applicants should apply before October 15, 2018.

Information about the department and the division

The Department of Computer Science and Engineering is a joint department at Chalmers University of Technology and the University of Gothenburg, with activities on two campuses in the city of Gothenburg. The department has around 240 employees from over 30 countries and enrolls a number of PhD students from more than 30 countries. Our research has a wide span, from theoretical foundations to applied systems development. We provide high quality education at Bachelor’s, Master’s and graduate levels, offering over 120 courses each year. We also have extensive national and international collaborations with academia, industry and society. For more information about the department, see

The Data Science and AI division is a new division in the department, reflecting how this area has grown considerably over the last years, recruiting new PhD students, post-docs and faculty members. The main research areas of the division are algorithms, machine learning, AI, and different applications of data science. The division has a solid network of collaborators, both academic and industrial, within and outside of Gothenburg, the home of Chalmers. We provide many courses in these areas, and are responsible for the Data Science and AI Master's program at Chalmers and Data Science Master's program at GU. The division also offers courses in mathematical modelling, optimization and related areas.

Information about the project

We are looking for a motivated PhD student to work on a challenging research project in adversarial neural networks and reinforcement learning. The main focus of the research will be on developing new generative deep learning models, such as generative adversarial networks (GAN) and variational autoencoders (VAE). We aim to develop novel approaches that are capable of producing in in a way that appropriately reflects underlying uncertainty. In reinforcement learning, generative models that incorporate uncertainty can enable efficient exploration of unknown environments without strong prior assumptions.

The student will work within the reinforcement learning group of Dr. Christos Dimitrakakis and will also have the opportunity to collaborate with our partner Kevin Smith and a PhD student to be hired at KTH, who will be more focused on the computer vision side of the problem. This collaboration will aim at developing reinforcment learning algorithms for experiment design in medical diagnostics.

The “Wallenberg AI, autonomous systems and software program” (WASP), which will possible fund this project, is launching a national initiative in Sweden ( to develop the foundations of AI and machine learning. This project is relevant to several of the four prioritized scientific subjects of the WASP-AI/MLX area. The main scope falls in the context of representation learning, wherein the research components are also connected to learning from small datasets and transfer learning. Moreover, the active representation learning aspect can be studied in the context of sequential decision making and adaptive information acquisition.

Major responsibilities

You will be enrolled in a graduate program in the Department of Computer Science and Engineering at Chalmers University of Technology. You are expected to develop your own ideas and communicate scientific results orally as well as in written form. In addition, the position will normally include 20% departmental work, mostly teaching duties. This will be a great opportunity to improve your scientific presentation skills.

Position summary

Full-time temporary employment. The position is limited to a maximum of five years. The current salary range for Chalmers PhD students is very competitive about 29 100 – 34 200 SEK per month.


Applicants must have a degree in computer science, mathematics, statistics or economics. They must have obtained a master’s degree, or expect to complete such a degree by January, 2019.

The applicant must be motivated to perform challenging research in reinforcement learning and learning theory. Prior knowledge in one of the following areas: machine learning, statistics, optimisation or game theory is required. Experience with reinforcement learning and learning theory is particularly welcome, although not required.

As Chalmers is a highly international environment, proficiency in written and spoken English is necessary. Chalmers continuously strives to be an attractive employer. Equality and diversity are substantial foundations in all activities at Chalmers. Chalmers is a major center for research and education in Sweden and is constantly considered as a top university of the country:

Our offer to you

Chalmers offers a cultivating and inspiring working environment in the dynamic city of Gothenburg.

Application procedure

The application should be marked with Ref 20180478 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.
- Why are you specifically interested in this PhD?
- Previous research fields and main research results.
- Future goals and research focus.

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: 15 October, 2018

For questions, please contact:

Christos Dimitrakakis, e-mail:

Dag Wedelin, e-mail:


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