PhD student in Machine Learning: Theoretically motivated deep learning

at KTH Royal Institute of Technology
Published December 4, 2022
Location Stockholm, Sweden
Category AutoML  
Job Type Scholarship  


School of Electrical Engineering and Computer Science at KTH

KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy.

Project description

At the Division of Computational Science and Technology at KTH we are seeking a new PhD student in Machine Learning / Computer Vision to handle scale-dependent information in image data.

In our research, we develop deep networks for processing image data that handle scaling transformations and other image transformations in a theoretically well-founded manner. Our research in this area comprises both theoretical modelling of the influence of image transformations on different architectures for deep networks as well as experimental evaluations of such networks on benchmark datasets to explore their properties. The work also comprises the creation of new benchmark datasets, to enable characterization of properties of deep networks that are not covered by existing datasets.

For examples of our previous work in this area, see

Within the scope of this PhD student position, you will work on and contribute to the research frontier regarding scale-covariant or scale-equivariant deep networks and/or deep networks parameterised in terms of Gaussian derivatives, on specific research topics that we choose together within the scope of the research project ”Covariant and invariant deep networks” that finances this position. The overall goal is to develop new architectures for deep networks that can generalise to scaling variations that are not spanned by the training data, and which can achieve higher robustness to variabilities in test data, as well as enable more efficient training with lower requirements concerning the amount of training data.

Third-cycle subject: Computer Science

Supervision: Prof. Tony Lindeberg

What we offer

Admission requirements

To be admitted to postgraduate education (Chapter 7, 39 § Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:

  • passed a second cycle degree (for example a master's degree), or
  • completed course requirements of at least 240 higher education credits, of which at least 60 second-cycle higher education credits, or
  • acquired, in some other way within or outside the country, substantially equivalent knowledge
  • to be admitted to the third-cycle education in Computer Science, the applicant must have passed courses resulting in at least 60 credits at minimum second-cycle level in Computer Science or other subjects deemed directly relevant to the chosen specialization.

In addition to the above, there is also a mandatory requirement for English equivalent to English B/6, read more here


In order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. Candidates will be assessed upon their ability to:

  • independently pursue his or her work
  • collaborate with others,
  • have a professional approach and
  • analyse and work with complex issues and

The candidate should have very good knowledge in mathematics (analysis and linear systems, which we use for modelling convolution transformations and geometric image transformations) as well as in structured programming to write code that is easy to use for making experiments with, maintain and develop and share with colleagues. You must have very good knowledge about programming deep networks in Python, PyTorch is meritorious.

Knowledge in computer vision and image analysis is strongly meritorious.

After the qualification requirements, great emphasis will be placed on personal competency.

Target degree: Doctoral degree

Information regarding admission and employment

Only those admitted to postgraduate education may be employed as a doctoral student. The total length of employment may not be longer than what corresponds to full-time doctoral education in four years ' time. An employed doctoral student can, to a limited extent (maximum 20%), perform certain tasks within their role, e.g. training and administration. A new position as a doctoral student is for a maximum of one year, and then the employment may be renewed for a maximum of two years at a time.

Union representatives

Contact information KTH's website.

Doctoral section (Students’ union on KTH Royal Institute of Technology)

Contact information section's website.


Apply for the position and admission through KTH's recruitment system. It is the applicant’s responsibility to ensure that the application is complete in accordance with the instructions in the advertisement.

Applications must be received at the last closing date at midnight, CET/CEST (Central European Time/Central European Summer Time).

Applications must include:

  • CV including your relevant professional experience and knowledge.
  • Application letter with a brief description of why you want to pursue research studies, about what your academic interests are and how they relate to your previous studies and future goals. (Maximum 2 pages long)
  • Copies of diplomas and grades from previous university studies and certificates of fulfilled language requirements (see above). Translations into English or Swedish if the original document is not issued in one of these languages. Copies of originals must be certified.
  • Representative publications or technical reports. For longer documents, please provide a summary (abstract) and a web link to the full text.

Other information

Striving towards gender equality, diversity and equal conditions is both a question of quality for KTH and a given part of our values.

For information about processing of personal data in the recruitment process please read here.

We firmly decline all contact with staffing, recruitment agencies and job ad salespersons.

Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.