PhD Projects

Explore Exciting PhD Opportunities in Academia and Industry

The SciLifeLab & Wallenberg National Program for Data-Driven Life Science (DDLS) has recently launched a competitive grant call for the PIs to suggest exciting data-driven research projects and training opportunities for PhD students in academia and industry.


Welcome to the DDLS Research School, a Swedish national initiative that aims to train scientists with high competence in data-driven life science and to meet the future needs within data-driven life science in R&D, industry, health care and society at large.

We are thrilled to announce a range of open PhD positions offering unique research opportunities in academia and industry.

PhD Positions:

Explore research opportunities in the following PhD projects in academia and industry focusing on the research areas of Cell and Molecular Biology, Evolution and Biodiversity, Precision Medicine and Diagnostics, Epidemiology and Biology of Infection. As a PhD student you are part of the DDLS Research School, that over the years will enrol 260 PhD students and over 200 post-docs. You will be given the opportunity to network with other PhD students, post-docs and PIs all over Sweden. Additionally, you will be trained to be an expert and a future leader within your field. Read more about the research school here.

The PhD positions are located at different universities within Sweden and the PhD student will also belong to the local research school, when applicable.

Available positions:

Cell and molecular biology:

Evolution and biodiversity:

Epidemiology and Biology of infection:
PhD position in bioinformatics and artificial intelligence

Precision Medicine and Diagnostics:
PhD student position in digital pathology
Doctoral (PhD) student position in Molecular Epidemiology with application in Precision Health

We invite motivated individuals to apply for the PhD positions. Please review the application guidelines and submission details on each project website. Please be aware of that the deadline for applications is different for different positions. We look forward to welcoming a new generation of PhD students who will shape the future of data-driven life science.

Approved Academic PhD Projects in Data-driven Life Science

Cell and molecular biology

Proposal titleMain PIAffiliationCo-PI(s)Affiliation
Multi-Modal Modeling of Spatial Biology DataJoakim LundebergKTHJens LagergrenKTH
Integrating single cell clonal, spatial and dissociated cell transcriptomics data for 3D neurodevelopmental reconstruction: a machine learning approachIgor AdameykoKISten Linnarsson
& Carolina Wählby
Novel, integrative AI methods for single-particle analysis of cryo electron microscopy data.Sebastian WestenhoffUUFredrik LindstenLiU
SpliceCode: the regulatory grammar controlling cell-type specific alternative splicingRickard SandbergKIAvlant NilssonKI
AfterFold: Conformational ensembles from experimental data using deep learningBjörn WallnerLiUNicholas PearceLiU
AI-enhanced virtual screens of chemical libraries to accelerate drug discoveryJens CarlssonUU
Cell and molecular biology: Approved projects

Evolution and biodiversity

Proposal titleMain PIAffiliationCo-PI(s)Affiliation
Data driven analyses of the nitrogen cycling microbiome for predictions and novel insights on mechanisms of nitrous oxide emissions from terrestrial ecosystems (TerraData)Sara HallinSLUChristopher JonesSLU
Can microbes distinguish friend from foe?Eric LibbyUmU Laura CarrollUmU
New probabilistic and AI methods for inferring recent and ongoing plant extinctionsAelys M. HumphreysSUDaniele Silvestro, Diana O. Fisher, Alexandre Antonelli, Jon NorbergUniversity of Fribourg; University of Queensland; Royal Botanic Gardens, Kew, GU, and SU
Developing biological weather forecasts for the digital twin of the oceanMatthias ObstGUTobias AndermannUU
Evolution and biodiversity: Approved projects

Epidemiology and Biology of infection

Proposal titleMain PIAffiliationCo-PI(s)Affiliation
Finding the prophages of Escherichia coli genomes and annotating the function of their genes using high-throughput AlphaFoldGemma AtkinsonLUAndrea FossatiKI
Predicting the future spread of antibiotic resistance genesErik KristianssonCTHJoakim Larsson & Johan Bengtsson-PalmeGU/CTH
Developing methods for inferring transmission chains and disease outbreak surveillance in a hospital settingPhilip GerleeCTHJon Edman Wallér GU
Epidemiology and Biology of infection: Approved projects

Precision Medicine and Diagnostics

Proposal titleMain PIAffiliationCo-PI(s)Affiliation
Prediction of Single Cell Drug Response for Precision Cancer Medicine using Foundational Deep Learning Models Kasper KarlssonKIJens Lagergren & Avlant NilssonKTH/KI
From computational analyses of big epigenetics data to novel biomarkers for precision medicine in type 2 diabetesCharlotte LingLUKarin Engström LU
Towards precision medicine for ischemic stroke: Integrating clinical, molecular omic, and neuroimaging data using deep and machine learning-based approachesChristina JernGUTara Stanne, Björn Andersson, & Markus SchirmerGU/GU/Harvard Medical Shool, USA
A precision study of molecular health and aging in Swedish population cohortsSara HäggKI Jochen Schwenk & Patrik MagnussonKTH/KI
Network-based cancer precision medicine using proteogenomics   Janne LehtiöKIWojciech Chacholski, Avlant Nilsson, & Ioannis SiavelisKTH/KI/KI
Improving prostate cancer diagnostics and prognostication using artificial intelligenceMartin EklundKIKimmo Kartasalo & Lars EgevadKI/KI
Deciphering Multiple Sclerosis: A Data-Intensive Approach to Unraveling Clinical and Molecular Complexities through Graph and Language ModelingIngrid KockumKINarsis Kiani & Ali ManouchehriniaKI/ Cambridge University/KI
Precision Medicine and Diagnostics: Approved projects

Approved Industrial PhD Projects in Data-driven Life Science

Proposal titleMain PIAffiliationCo-PI(s)CompanyOther Co-PI(s)Affiliation
Tailored Protein Panel Composition in Biomarker Discovery Using Concrete AutoencodersLukas KällKTHLina Hultin-RosenbergOlink Proteomics ABFredrik Edfors, Hossein Azizpour, & Linn FagerbergKTH/KTH/Olink Proteomics AB
Development and validation of AI-based histopathology phenotyping solutions to scale and accelerate breast cancer researchMattias RantalainenKIStephanie Robertson & Philippe WeitzStratipath ABBojing LiuKI
Automated generation of renal pathology endpoints and reportsKevin SmithKTH Magnus Söderberg AZAnnika Östman Wernerson KI
Scaling up single molecule variant-detection for aquatic pathogen surveillanceStefan BertilssonSLULiza LöfReadily Diagnostics
Drugging the undruggable: bridging AI and MD to discover small molecule binders for difficult-to-drug targets Erik LindahlSUOla EngkvistAZRocio Mercado & Werngard CzechtizkyCTH/AZ
Improving Treatment Response Evaluation in Whole-Body CT-Imaging by Automated Quantitative Assessment of Tumor Burden and Lesion-Wise Analysis in Metastatic CancerJoel KullbergUUSimon Ekström Antaros Medical ABHåkan Ahlström, Johan Öfverstedt, & Elin LundströmUU/UU/UU
Towards precision medicine in obesity with high cardiometabolic risksRashmi PrasadLUSara HanssonAZ
Call for Industrial PhD Projects in Data-driven Life Science: Approved projects

We look forward to welcoming a new generation of PhD students who will shape the future of data-driven life science.

For questions please contact:

Last updated: 2024-04-18

Content Responsible: Johan Inganni(