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
Currently we do not have any open PhD positions.
Previously Approved Academic PhD Projects in Data-driven Life Science 2024
Cell and molecular biology
| Proposal title | Main PI | Affiliation | Co-PI(s) | Affiliation |
|---|---|---|---|---|
| Multi-Modal Modeling of Spatial Biology Data | Joakim Lundeberg | KTH | Jens Lagergren | KTH |
| Integrating single cell clonal, spatial and dissociated cell transcriptomics data for 3D neurodevelopmental reconstruction: a machine learning approach | Igor Adameyko | KI | Sten Linnarsson & Carolina Wählby | KI/UU |
| Novel, integrative AI methods for single-particle analysis of cryo electron microscopy data. | Sebastian Westenhoff | UU | Fredrik Lindsten | LiU |
| SpliceCode: the regulatory grammar controlling cell-type specific alternative splicing | Rickard Sandberg | KI | Avlant Nilsson | KI |
| AfterFold: Conformational ensembles from experimental data using deep learning | Björn Wallner | LiU | Nicholas Pearce | LiU |
| AI-enhanced virtual screens of chemical libraries to accelerate drug discovery | Jens Carlsson | UU |
Evolution and biodiversity
| Proposal title | Main PI | Affiliation | Co-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 Hallin | SLU | Christopher Jones | SLU |
| Can microbes distinguish friend from foe? | Eric Libby | UmU | Laura Carroll | UmU |
| New probabilistic and AI methods for inferring recent and ongoing plant extinctions | Aelys M. Humphreys | SU | Daniele Silvestro, Diana O. Fisher, Alexandre Antonelli, Jon Norberg | University of Fribourg; University of Queensland; Royal Botanic Gardens, Kew, GU, and SU |
| Developing biological weather forecasts for the digital twin of the ocean | Matthias Obst | GU | Tobias Andermann | UU |
Epidemiology and Biology of infection
| Proposal title | Main PI | Affiliation | Co-PI(s) | Affiliation |
|---|---|---|---|---|
| Finding the prophages of Escherichia coli genomes and annotating the function of their genes using high-throughput AlphaFold | Gemma Atkinson | LU | Andrea Fossati | KI |
| Predicting the future spread of antibiotic resistance genes | Erik Kristiansson | CTH | Joakim Larsson & Johan Bengtsson-Palme | GU/CTH |
| Developing methods for inferring transmission chains and disease outbreak surveillance in a hospital setting | Philip Gerlee | CTH | Jon Edman Wallér | GU |
Precision Medicine and Diagnostics
| Proposal title | Main PI | Affiliation | Co-PI(s) | Affiliation |
|---|---|---|---|---|
| Prediction of Single Cell Drug Response for Precision Cancer Medicine using Foundational Deep Learning Models | Kasper Karlsson | KI | Jens Lagergren & Avlant Nilsson | KTH/KI |
| From computational analyses of big epigenetics data to novel biomarkers for precision medicine in type 2 diabetes | Charlotte Ling | LU | Karin Engström | LU |
| Towards precision medicine for ischemic stroke: Integrating clinical, molecular omic, and neuroimaging data using deep and machine learning-based approaches | Christina Jern | GU | Tara Stanne, Björn Andersson, & Markus Schirmer | GU/GU/Harvard Medical Shool, USA |
| A precision study of molecular health and aging in Swedish population cohorts | Sara Hägg | KI | Jochen Schwenk & Patrik Magnusson | KTH/KI |
| Network-based cancer precision medicine using proteogenomics | Janne Lehtiö | KI | Wojciech Chacholski, Avlant Nilsson, & Ioannis Siavelis | KTH/KI/KI |
| Improving prostate cancer diagnostics and prognostication using artificial intelligence | Martin Eklund | KI | Kimmo Kartasalo & Lars Egevad | KI/KI |
| Deciphering Multiple Sclerosis: A Data-Intensive Approach to Unraveling Clinical and Molecular Complexities through Graph and Language Modeling | Ingrid Kockum | KI | Narsis Kiani & Ali Manouchehrinia | KI/ Cambridge University/KI |
Approved Industrial PhD Projects in Data-driven Life Science
| Proposal title | Main PI | Affiliation | Co-PI(s) | Company | Other Co-PI(s) | Affiliation |
|---|---|---|---|---|---|---|
| Tailored Protein Panel Composition in Biomarker Discovery Using Concrete Autoencoders | Lukas Käll | KTH | Lina Hultin-Rosenberg | Olink Proteomics AB | Fredrik Edfors, Hossein Azizpour, & Linn Fagerberg | KTH/KTH/Olink Proteomics AB |
| Development and validation of AI-based histopathology phenotyping solutions to scale and accelerate breast cancer research | Mattias Rantalainen | KI | Stephanie Robertson & Philippe Weitz | Stratipath AB | Bojing Liu | KI |
| Automated generation of renal pathology endpoints and reports | Kevin Smith | KTH | Magnus Söderberg | AstraZeneca | Annika Östman Wernerson | KI |
| Scaling up single molecule variant-detection for aquatic pathogen surveillance | Stefan Bertilsson | SLU | Liza Löf | Readily Diagnostics | ||
| Drugging the undruggable: bridging AI and MD to discover small molecule binders for difficult-to-drug targets | Erik Lindahl | SU | Ola Engkvist | AstraZeneca | Rocio Mercado & Werngard Czechtizky | CTH/AstraZeneca |
| Improving Treatment Response Evaluation in Whole-Body CT-Imaging by Automated Quantitative Assessment of Tumor Burden and Lesion-Wise Analysis in Metastatic Cancer | Joel Kullberg | UU | Simon Ekström | Antaros Medical AB | Håkan Ahlström, Johan Öfverstedt, & Elin Lundström | UU/UU/UU |
| Towards precision medicine in obesity with high cardiometabolic risks | Rashmi Prasad | LU | Sara Hansson | AstraZeneca |
For questions please contact: ddls-rs@scilifelab.se
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 a web based template and send 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 a web based template and send 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 approved by the board in September.