Welcome to the PhD program

Introduction

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.

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.

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, and the DDLS program 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

Academic positions

Cell and Molecular biology

Epidemilogy and biology of infection

Evolution and biodiversity

Precision Medicine and Diagnostics

Industrial positions

Approved Academic PhD Projects in Data-driven Life Science 2025 Call

In the table below, universities are listed using their common abbreviations for clarity and simplicity. Here is what each abbreviation stands for, in alphabetical order:

Chalmers: Chalmers University of Technology; GU: University of Gothenburg; KI: Karolinska Institute; KTH: Royal Institute of Technology; LiU: Linköping University; LU: Lund University; SLU: Swedish University of Agricultural Sciences; SU: Stockholm University; UU: Uppsala University; UmU: Umeå University; ÖU: Örebro University

Cell and molecular biology

Proposal title	Main PI	Co-PI
Deep Probabilistic Models of MS2 Fragmentation for Improved Spectrum Matching	Lukas Käll, KTH	Fredrik Edfors, KTH
A virtual cellular reprogramming screening platform	Filipe Pereira, LU	Ilia Kurochkin, LU; Fabian Theis, Institute of Computational Biology, Helmholtz Zentrum München, Germany
Data-Driven Modeling of Protein Conformational Dynamics through Integration of LiP-MS and Deep Learning	Ilaria Piazza, SU	Arne Elofsson
Contextual modelling of IDP interactions in cancer – from proxiomes to structural assemblies	Maria Sunnerhagen, LiU	Björn Wallner, LiU; Brian Raught, University of Toronto; Alexandra Ahlner, LiU
Beyond the Tissue Section: Reference-Free 3D Spatial Omics for Single-Cell Resolution in Intact Organs	Patrik Ståhl , KTH	Ian Hoffecker, KTH
Mapping single cell expression networks with connectogenomics	Ian Hoffecker, KTH	Rickard Sandberg, KI; Sefania Giacomello, KTH; Simon Koplev, KTH
Computation analysis of cerebellar network dynamics in learning	Anders Rasmussen, LU	Erik Martens, LU
Deep learning-based image analysis for the development of next generation RNA therapeutics	Anders Wittrup, LU	Niels Christian Overgaard, LU

Evolution and biodiversity

Proposal title	Main PI	Affiliation
Decoding the high-dimensional genotype-phenotype space by multi-objective optimization and machine learning	Jonas Warringer, GU	Gianni Liti, University of Nice – France; Kaisa Thorell, GU
Duplicity: the catalysts of polyploid evolution resolved through integrative pangenomics	Levi Yant, SLU	Filip Kolář, Charles University, Prague, CZ
Evolution of protein domain architectures in the AI-era.	Arne Elofsson, SU	Jens Lagergren, KTH; Gemma Catherine Atkinson, LU; Lisandro Milocco, SU
The code that shapes the body: AI and digital phenotyping to decode evolutionary morphology in domesticated animals.	Elin Hernlund, SLU	Hedvig Kjellström, KTH; Sofia Mikko, SLU

Epidemiology and Biology of infection

Proposal title	Main PI	Co-PI
CRITICAL AI – Comprehensive Research on InfecTIons Across the Lifespan using AI	Anne-Marie Fors Connolly, UmU	Martin Rosvall, UmU; Tommy Löfstedt, UmU
Towards a mechanism-aware multi-label prediction framework for antimicrobial peptides	Michaela Wenzel, Chalmers	Annikka Polster, Chalmers
Please pass the plasmid: single-cell surveillance of antimicrobial resistance genes in microbiomes	Johan Henriksson, UmU	Laura Carroll, UmU; Tommy Löfstedt, UmU

Precision Medicine and Diagnostics

Proposal title	Main PI	Co-PI
Guiding Breast Cancer Therapy Through AI-Interpreted Patient-Centric Protein Modules	Henrik Johansson, KI	Andre Freitas, Idiap Research Institute, Switzerland, Department of Computer Science, University of Manchester, UK AI Group Leader, National Biomarker Centre (NBC), CRUK Manchester Institute, UK; Janne Lehtiö, KI
Integrated Analysis of ctDNA	Jens Lagergren, KTH	Johan Hartman, KI
Learning to find the needle in the haystack: Heterogeneous Hierarchical  Multiple Instance Learning for Computational Pathology	Joakim Lindblad, UU	Masood Kamali-Moghaddam, UU
Data-driven discovery of molecular subtypes and resistance mechanisms in chronic lymphocytic leukemia	Richard Rosenquist Brandell, KI	Janne Lehtiö, KI; Leily Rabbani, KI; Daniel Hägerstrand, KI
Blood-based biomarkers and molecular changes in preclinical dementia	Ida Karlsson, KI	Sara Hägg, KI; Karolina Kauppi, UmU & KI
Early detection of lung cancer through fusion of chest CT and clinical variables	Anders Eklund, LiU	Tomas Bjerner, LiU; Chunliang Wang, KTH
Multi-omics analyses of colorectal cancers	Tobias Sjöblom, UU	Marcel Tarbier, UU
Uncertainty-aware extraction of clinical information from unstructured medical reports	Sara Hamis, UU	Pär Stattin, UU; Ekta Vats, UU
Mapping the Epigenetic Landscape of Multiple Sclerosis: Integrative and Interpretable AI Approaches to Disease Mechanisms and Progression	Maja Jagodic, KI	Narsis Kiani, KI; Pietro Liò, Cambridge University, United Kingdom
Multiomics analysis of immune clonal niches in breast cancer	Camilla Engblom, KI	Joakim Lundeberg, KTH

Approved Industrial PhD Projects in Data-driven Life Science 2025 Call

Proposal title	Main PI	Industry Co-PI(s)
Development of machine-learning methods to improve binding-affinity estimates	Ulf Ryde, LU	Jon Paul Janet, Molecular AI, Discovery Sciences, R&D, AstraZeneca
Data-Driven Modeling of Human Biology Using Real-World Multimodal Laboratory and Registry Data	Wei Ouyang, KTH	Sebastian Bujwid, ABC Labs
From Explainable to Actionable and Reversible Epigenetics: A Transformer-based Foundation Model for DNA Methylation in Longitudinal Cohorts	Sara Hägg, KI	Mika Gustafsson, PredictMe AB
Accelerating Orally Bioavailable Cyclic Peptide Drug Discovery with AI	Patrick Bryant, SU	Dr. Alessandro Tibo, AstraZeneca
Biomarker driven precision diagnostics	Anders Ståhlberg, GU	Mikael Kubista, MultiD Anlyses AB
Cross-Species Representation Learning for Translational Disease Insights	Kevin Smith, KTH	Christos Matsoukas, AstraZeneca
RIBO-LINC: Linking data-driven solutions and RNA innovation for liver and cardiometabolic health	Claudia Kutter, KI	Prof Dr Li-Ming Gan, Ribocure

Previously approved PhD Projects in Data-driven Life Science

PhD Recruitment Process for PIs

Academia
PIs need to start the recruitment process according to the local regulations at their university. The final selected candidates need to be approved separately for funding by the DDLS program.

Before the recruitment of the PhD candidate, the PI is requested to summarize the process in our web-based template, PhD Student Recruitment Summary (PSRS), and send it to us. Based on this documentation, the funding to the final selected candidates will be approved by the DDLS program.

The PIs will also need to sign a Terms and Conditions document for their commitment to PhD project as part of the DDLS program. This document will be sent to the PIs once the PhD candidate has been approved by the DDLS program.

Industry
PIs need to start the recruitment process according to the local regulations at their university. The final selected candidates need to be approved separately for funding by the DDLS program.

Before the recruitment of the PhD candidate, the PI is requested to summarize the process in our web-based template, PhD Student Recruitment Summary (PSRS), and send it to us. Based on this documentation, the funding to the final selected candidates will be approved by the DDLS program.

The terms and conditions regarding the funding of the industrial PhD projects will be replaced with a decision letter, which will be sent to the industrial PIs once the PhD candidate has been approved by the DDLS program.

The academic PIs will also need to sign a Terms and Conditions document for their commitment to PhD project as part of the DDLS program. This document will be sent to the PIs once the PhD candidate has been approved by the DDLS program.

Deadline for the Recruitment Process
Please note the following fixed deadlines for PhD students to be in place:

• 31 October (primary deadline)
• 28 February (final and absolute deadline)

All recruitment process must be completed in time to ensure that the selected PhD students are in place by these deadlines. If, for any reason, your PhD students are not in place by the final deadline, you must contact us as soon as possible to discuss next steps.