DDLS Research School Postdoc call 2026

Call for Academic and Industrial Postdoctoral Fellowships in Data-Driven Life Science 2026

Generic Description of the DDLS Postdoc Program

The SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS) is a national 12-year initiative funded with a total of 3,3 billion SEK from the Knut and Alice Wallenberg Foundation. The purpose of the program is to recruit and train the next generation of data-driven life scientists and to establish globally leading computational and data science capabilities in life sciences in Sweden.

There are four strategic research areas within DDLS: Cell and Molecular Biology, Evolution and Biodiversity, Precision Medicine and Diagnostics, and Epidemiology and Biology of Infection. The program supports research, infrastructure development, and education in data-driven life sciences.

The national DDLS Research School (RS) connects postdoctoral researchers and PhD students in the program, providing a shared national framework for training and interaction. A central role of the research school is to support networking through annual meetings, seminars, and other national events that connect fellows across the DDLS research areas.

The DDLS Postdoc Program offers two tracks for postdoctoral researchers: an academic track and an industry track. The academic track is designed for postdoctoral researchers who will be employed at Swedish universities or the Swedish Museum of Natural History, while the industry track is intended for postdoctoral researchers who will be employed by companies in the life sciences sector in Sweden. The program aims to connect them with strong local research environments at universities, as well as with the national DDLS program.

Once admitted, candidates will be enrolled as members of the DDLS RS and are expected to take part in DDLS RS activities (networking events, courses, scientific visits, etc.).

We are now launching a call for candidates, along with potential supervisors, to suggest exciting data-driven research projects in the four strategic research areas of DDLS. In this call, 15 academic and 7 industrial postdoc projects will be awarded.

What is Data-driven life science?

Data-driven life science is a field of research that utilizes data, computational methods, and artificial intelligence to investigate biological systems and processes. This can include assembling, sharing, integrating, and advanced analysis of large amounts of data from diverse sources, including experiments, observations, and simulations, to gain a deeper understanding of how living organisms function.

For a postdoc project to be considered data-driven, it must have a clear data science component, such as the use of advanced data analysis techniques, from statistics to machine learning, involving either method development or novel application of data science methods to life science problems. Projects that only involve laboratory research or depend solely on the acquisition of large amounts of new biological data, such as from laboratory experiments or patient materials, will not be given priority. However, laboratory research can be used to validate or complement data-driven insights.

Requirements for projects

Project proposals in the four strategic research areas of DDLS (Cell and Molecular Biology, Evolution and Biodiversity, Precision Medicine and Diagnostics, and Epidemiology and Biology of Infection) are welcome. The funded projects should align with the DDLS strategy, offer a novel and original data-driven perspective, possess high scientific quality, and combine life science and data science topics, providing an excellent training environment.

Each 2-year project receives a total of 2 MSEK in KAW funding. The selected candidates will be employed by the university or the Swedish Museum of Natural History for the academic track, and by the company for the industrial track. The employer will be responsible for any necessary co-funding (as described below), which should be ensured by a letter of commitment from the potential employer.

The DDLS RS will provide a national matchmaking site where applicants can find academic supervisors and industrial partners with whom to collaborate. It is the responsibility of the applicants to find suitable supervisors and industrial partners; however, candidates are free to use any other means to find potential supervisors and industrial partners, if preferred.

Up to ten percent of the postdoc’s working time may be allocated to duties outside the funded project, such as teaching or institutional service. Any such duties must be agreed with the host and kept within this limit.

Requirements for Project Supervision

One academic supervisor must be presented in the project application. For the industrial track, in addition, one company-employed supervisor (hereinafter referred to as the industrial supervisor) must be presented in the project application.

The academic supervisor must have a primary employment at a Swedish university or the Swedish Museum of Natural History during the suggested postdoc period. An industrial supervisor must also have secured employment (minimum 80% of the time) during the suggested postdoctoral period at the company. The supervisors are expected to take responsibility and provide guidance to the postdoc throughout the grant period.

For the industrial track, the academic supervisor can be involved in the company, but not as the CEO of the company with which they are applying. The academic supervisor can be a partner in the company they apply together with, but not the majority owner. Please note that research institutes and various types of state, regional, and municipal works, as well as bodies, do not qualify as industry and therefore cannot receive funding from the industrial track. The industrial partners/companies must be based in Sweden or have a significant presence in Sweden (such that the project benefits Sweden in the long term). The company must adhere to Swedish laws and regulations.

The call allows for co-supervision by additional academic and/or industrial supervisors, but this is not a requirement. The supervisor(s) should provide a research environment that enables the postdoc to interact with expertise in data science as well as life science.

Both academic and industrial supervisors may act as supervisors, but each person may appear as a main academic or industrial supervisor on only one application per call. A company may support multiple applications, provided that it assigns different industrial supervisors to each applicant and that the projects are significantly different.

Project participants (supervisors and successful applicants) are expected to be active contributors and participants in the national DDLS community events, training activities, seminars, and symposia organized by the DDLS RS.

Who can apply?

To be eligible for this call, applicants must hold a doctoral degree (or an equivalent foreign degree assessed as equivalent to a doctoral degree) prior to commencing the postdoc position.

To be eligible for employment as a result of this call at Swedish universities or the Swedish Museum of Natural History, candidates should primarily have obtained a doctoral degree not more than three years previously. For the purpose of calculating the three-year framework period, the starting point is the deadline for applications. Exceptions are allowed for documented parental leave, compulsory military service, medical leave, or union work.

To be eligible for employment as a result of this call at Swedish universities or the Swedish Museum of Natural History, the applicant should not previously been employed as a postdoc researcher for more than one year with the same employer.

In addition, it is considered a strong merit if the applicant:

Applies to the call with a different proposed academic supervisor than the candidate’s main PhD advisor.

Furthermore:

Each applicant may submit only one application to the call.
Each academic supervisor may support at most one academic application and one industrial application in the call.
Each industrial supervisor can support only one applicant in the call. However, the same company may appear on several applications, provided that each applicant is planned to be supervised by a different industrial supervisor and that the projects are significantly different.

Key Dates

12 Jan 2026 — Call opens
31 Mar 2026 — Application deadline
Apr–Jun 2026 — Evaluation and interviews
15 Jun 2026 — Funding Decision
1 Oct 2026 — Suggested start of employment

A more detailed timeline is provided in the document timeline.

Required Documents

A candidate may submit only one application. The application shall be written in English and must include the following documents (Arial, 11 pt, 2.5 cm margins):

Proposal abstract (300 words)
Research plan (max. 4 pages):
- Main objectives, background, methodology, impact, and future potential of the project.
- Description of data-driven methods used and/or developed within the project.
- Budget, time plan, project risks, and mitigation plan.
FAIR – past contributions: Self-assessment of previous activities in line with FAIR principles (e.g., data sharing, open repositories, software development, standards compliance) and FAIR – future commitments: Statement on how the candidate will ensure FAIR usage and contributions within the proposed project (e.g., dataset deposition, open-source code, metadata standards, interoperability with existing infrastructures) (max 0.5 page).
Ethics self-assessment (in template form; if ethical approval is needed, this must be clearly stated) (1 page, if needed).
Curriculum vitae of the candidate, including a list of publications, and two reference persons (max. 2 pages).
Curriculum vitae of academic, and when applicable, industrial supervisor, including relevant publications. (max. 2 pages each).
Curriculum vitae of any co-supervisors. (max. 1 page each).
Supporting letter from a Group Leader / hosting PI, explaining how the postdoc will fit in the lab. (max 1 page.)
For academic positions: a letter of commitment from the Head of Department/faculty stating that all necessary co-funding needed for a KAW-funded project will be covered by the department.
For industry positions, two Letters of commitment. One from the research director (or equivalent) of the company, stating that the company will take responsibility for any necessary co-funding.

Important note: Project proposals that do not use the provided templates or exceed page limits will receive a lower score than applications that do.

Evaluation Process

Each proposal is reviewed by a panel of external experts. Shortlisted candidates are invited to an online interview, which might result in a reranking of the candidates. The final funding decision is made by the SciLifeLab Director together with the Chair of the SciLifeLab Board. Detailed information on the evaluation criteria and process is found in the document evaluators_guide.

Employment and Admission Process

Successful applicants will be employed by their host university or the Swedish Museum of Natural History for the academic track, and by the company for the industrial track. It is each employer’s responsibility to ensure that the candidate is employed under conditions in accordance with Swedish law. Applicants should not apply directly to the university at this stage; instead, they should apply through this call. The employment will be for a period of two years. The program may allow for applications for one-year extensions for academic positions; however, the procedures for such applications have not been finalized.

Financial Framework

Each awarded postdoc is funded for a period of two years.

Academic Track

The grants will be funded by KAW. The supervisors, departments, or faculties are responsible for any necessary co-funding at each partner organisation.
Each 2-year project receives a total of 2 MSEK in KAW funding.
The funding is primarily intended to cover salaries and salary-related direct and indirect costs.
A maximum of 20% of the granted amount can be used for premises and overhead costs.
Payroll overhead (LKP) can only be covered up to a maximum of 52.5% on personnel costs.
The DDLS program requires academic partners to recruit postdocs through employment rather than scholarships. This is because scholarships, unlike employment, do not provide access to the same social security, pension rights, or reimbursement frameworks that may have tax implications.
Reimbursement is handled through requisitions submitted to KTH/SciLifeLab, which will coordinate the payment transfer process. Further information on the financial process and reporting templates will be provided at a later time.

Industrial Track

The grants will be funded by KAW. The industrial supervisors, departments are responsible for any necessary co-funding at each partner organisation.
Each 2-year project receives a total of 2 MSEK in KAW funding.
The funding is primarily intended to cover salaries and other direct costs.
The employer will send VAT-excluded invoices semi-annually directly to KTH/SciLifeLab. Detailed information about the financial process and reporting templates will be provided at a later stage.

Q1: Does an applicant need to be purely a bioinformatician?
A: No. Many types of applicants can apply. Having quantitative components in your formal or prior education is a strong merit.

Q2: What is the assessment balance between candidates, projects, supervision, training environments, etc.?
A: The guide for evaluators lists this clearly. Generally, there is a very strong emphasis on the candidate’s profile, as the program aims to recruit talent.

Q3: Should both industrial and academic supervisors be listed in the application?
A: Yes, it is advisable to list both the industrial host and academic supervisor in the short description.

Q4: Can we apply if we get a PhD before October 1st?
A: Yes, but you cannot start the position unless you have the PhD title before October 1st.

Q5: How important is a background in data science? Can an applicant with a mainly biological background apply?
A: A data science background is an clear advantage but not a formal requirement. The project must have a clear data science component.

Q6: When should a PI decide which applicant to support? Should candidates be interviewed before they apply?
A: The PI should decide as soon as possible and is responsible for selecting the best candidate. Interviewing candidates before they apply is recommended.

Q7: Does previous work under a scholarship count as employment?
A: Funding through scholarships is not to be considered as employment.

Q8: What are the funding conditions regarding overheads and premises?
A: Only 20% of the total funding can be used for indirect costs and premises, with a maximum of 400,000 SEK over two years.

Q9: What are potential pitfalls in the application or evaluation process?
A: The project should be feasible with a clear outcome or conclusive result within the two-year period.

Q10: Should the project be standalone or part of a bigger ongoing project?
A: Both are acceptable. The project should have a clear, feasible outcome.

Q11: Does having current DDLS PhD students in the group count as a pro or con?
A: Neither. There are no requirements excluding applicants hosting current DDLS PhD students.

Q12: Does the call focus more on applied or fundamental science?
A: Both are equally valued. Social relevance and societal impact are important.

Q13: Is a postdoc who has already worked six months in the lab eligible?
A: Yes, but there are deadlines and rules about how long after the PhD you can apply. Exact details are in the conditions.

Q14: Can co-funding cover overheads exceeding the 20% cap? Can other funding from KAW be used?
A: Co-funding is mainly to cover overheads exceeding the 20% cap. KAW funding cannot be used for this purpose.

Q15: If the decision is for two years of employment, can co-funding extend it to three years?
A: This is under discussion and not yet decided.

Q16: How much lab work is appropriate in data-driven proposals?
A: There is no fixed limit. The more non-lab work, the better, but some lab work to validate results is encouraged.

Q17: Can the collaborative part of the project be international?
A: Yes, but the employment must be Swedish.

Q18: Can SciLifeLab support programs be mentioned as a resource to demonstrate strong support in data-driven science?
A: Yes, but the project must show independence in the data science part.

Q19: If the applicant does not have a solid bioinformatics background, can support programs substitute for this?
A: Yes, but the project must still have a strong data science component and show some independence.