School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH

Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health and global ecosystems. The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) aims to recruit and train the next-generation of data-driven life scientists and to create globally leading computational and data science capabilities in Sweden. The program is funded with a total of 3.1 billion SEK (about 290 MUSD) over 12 years from the Knut and Alice Wallenberg (KAW) Foundation.

During 2024 the DDLS Research School will be launched with the recruitment of 20 academic and 7 industrial PhD students. During the course of the DDLS program more than 260 PhD students and 200 postdocs will be part of the Research School. The DDLS program has four strategic areas: cell and molecular biology, evolution and biodiversity, precision medicine and diagnostics, epidemiology and biology of infection. For more information, please see: Data-Driven Life Science (DDLS) - SciLifeLab 

The future of life science is data-driven. Will you be part of that change? Then join us in this unique program!

Project description

Third-cycle subject: Biotechnology

At KTH Royal Institute of Technology/SciLifeLab, we are looking to fill the position as PhD student in data-driven cell and molecular biology covers research that fundamentally transforms our knowledge about how cells function by peering into their molecular components in time and space, from single molecules to native tissue environments.

The subject concerns spatial biology, which encompasses a wide range of technologies that quantify different types of biomolecules in situ. Available technologies provide information about distinct aspects of tissue anatomy, such as its morphology, genome, transcriptome, proteome, and metabolome. Comprehensively characterizing a tissue requires combining multiple technologies, which can be costly or challenging experimentally. The student will develop methods for multi-modal modeling of spatial biology data that integrate diverse data types and can be used for cross-modality data transfer.

Multi-modal generative models are likely to become an increasingly crucial biological research tool. Due to the complex nature of many biological systems, any single modality is unlikely to describe their properties fully. Therefore, leveraging a learned knowledge base that spans multiple modalities and anatomical conditions is crucial for interpreting the data and accelerating discovery. For this purpose, the PhD student will employ our unique spatial multimodal datasets of tissue morphology, gene expression, genome integrity, and metabolites. The primary goal for the PhD student is to explore spatial datasets and develop data-driven methods to improve our understanding of biological systems in health and disease. The position also includes teaching and other duties in the department.

Supervision: Prof Joakim Lundeberg is proposed to be a main supervisor, and Prof Jens Lagergren to be the co-supervisor. Decisions are made on admission

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

It is important that you have solid programming skills for handling biological big data analysis. You should:

  • have experience working on machine learning systems in PyTorch or JAX
  • have experience in interpretable methods in machine learning
  • have experience in using version control systems like Git and public repositories like GitHub or GitLab
  • have worked with data from molecular biology technologies, such as genomics, transcriptomics, or proteomics, in the context of human diseases
  • be familiar with foundational models
  • have experience in designing, conducting, and analyzing experimental research
  • show ability to formulate research questions and hypotheses

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


In order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. During the selection process, 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.

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. In the case of studies that are to be completed with a licentiate degree, the total period of employment may not be longer than what corresponds to full-time doctoral education for two years.

Union representatives

You will find contact information for union representatives on KTH's website.

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

You will find contact information for doctoral section on the section's website.

To apply for the position

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 the following elements:

  • 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.

The position may include security-sensitive activities. To become authorized, you therefore need to pass a possible security check.

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
Contract type Full time
First day of employment According to agreement
Salary Monthly salary according to KTH's doctoral student salary agreement
Number of positions 1
Full-time equivalent 100%
City Solna
County Stockholms län
Country Sweden
Reference number C-2024-0699
  • Joakim Lundeberg,
Published 02.May.2024
Last application date 10.Jun.2024 11:59 PM CEST

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