KTH Royal Institute of Technology, School of Computer Science and Communication

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 in architecture, industrial management, urban planning, history and philosophy.

For information about the School of Computer Science and Communication, please visit https://www.kth.se/en/csc.

Department information

The position will be formally placed with the department for Computational Science and Technology (CST) at KTH, but work will be carried out at the Science for Life Laboratory. The CST department conducts research to understand and model physical and biological systems using computational techniques, which require efficient, high performance algorithms and implementations together with advanced visual analysis capabilities. For more information go to https://www.kth.se/en/csc/forskning/cst.

The Science for Life Laboratory, SciLifeLab, is a national center for molecular biosciences with focus on health and environmental research. The center combines frontline technical expertise from various disciplines with advanced knowledge of translational medicine and molecular bioscience. SciLifeLab has a mission from the Swedish government to be an internationally leading center for large-scale analyses in molecular life sciences targeting research in health and environment and to run infrastructure to support researchers. For more information, visit https://www.scilifelab.se/.

Our research group collaborates closely with physicians and biologists at SciLifeLab and in Karolinska Institutet one of the world’s leading medical schools, to develop algorithms and software to automatically analyze and interpret microscopic and medical images (https://www.kth.se/profile/ksmith).

Job description

Deep learning, as a field of machine learning, has dramatically pushed the performance of many intelligent systems, but many important questions remain open for research. How should one interpret its decision making process? Can one successfully learn deep learning models without large-scale annotated data? What are the limits of its application to other fields?

The role of the doctoral student will be to focus on developing theoretical advances regarding these research questions and/or applying them to general computer vision and, to a lesser extent, natural language processing. A secondary aspect involves applications with medical data. Medical data analysis is attracting attention from top players in various fields as more data and resources become available (such as Medical Imagenet by Stanford University). We will look for ways to apply the methods we develop in many exciting medical applications such as automatic diagnosis, personalized drug discovery, genetic analysis, and so forth.

The specific research topics may include but are not limited to using adversarial training for unsupervised and semi-supervised learning as well as domain adaptation, uncertainty estimation of a deep network’s output, understanding deep networks and its inner workings, and applying state-of-the-art models to highly impactful medical applications such as cancer prediction in medical data. Should the student be willing and experienced enough in deep learning, she/he will have some freedom to steer the direction of research.

This is a four (4) year time-limited position with full funding and support for travel to conferences, etc. It can be extended up to five (5) years with the inclusion of a maximum of 20% departmental duties, typically teaching.

In order to be employed, you must apply and be accepted as a doctoral student at KTH. The starting date is open for discussion, though we would like the successful candidate to start as soon as possible. 


A Bachelor of Science degree in Computer Science or a closely related field is required. Preference will be given to applicants with a Master's degree or current Master students who are about to complete their degree.

Applicants should have a good knowledge of English and ability to express themselves clearly both in speech and writing. The successful candidate must be strongly motivated for doctoral studies, must have demonstrated the ability to work independently and to perform critical analysis. They must also possess excellent cooperative and communication skills.

Of highest importance is prior experience/education in both theory and practice of machine learning, specially deep learning. We prefer experienced users of deep learning frameworks such as TensorFlow, Torch, Keras, Theono, Caffe, CNTK, MXNet. Proficiency in one or two scientific computing language(s) (R, Matlab, Python) is required.

Also desirable is prior experience with parallel programming environments, familiarity with Linux administration, experience with image analysis (especially medical or microscopy), experience with C++ programming, and working with remote HPC and cloud services.

Trade union representatives

You will find contact information to trade union representatives at KTH's webpage.


Log into KTH's recruitment system in order to apply to this position. You are the main responsible to ensure that your application is complete according to the ad.

Your complete application must be received at KTH no later than the last day of application, midnight CET/CEST (Central European Time/Central European Summer Time).

Application shall include the following documents:

1. Statement of interest and brief description of experience in machine learning, and/or deep learning, computer vision, and natural language processing.
2. Curriculum vitae
3. Transcripts from university/university college
4. Letter of recommendation and contact information from two references
5. An example of the applicant’s original technical writing, e.g., thesis, technical report, or scientific paper

Please observe that all material needs to be in English, apart from official documents.


We firmly decline all contact with staffing and 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.

Type of employment Temporary position longer than 6 months
Contract type Full time
First day of employment According to agreement
Salary Monthly salary according to KTH´s PhD student salary agreement
Number of positions 1
Working hours Heltid
City Solna
County Stockholms län
Country Sweden
Reference number D-2017-0457
  • Kevin Smith/Assistant Professor, ksmith@kth.se, +46 8 790 64 37
  • Maria Engman / HR Administrator, maengm@kth.se
Published 15.Jun.2017
Last application date 15.Aug.2017 11:59 PM CET

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