The following PhD projects have been approved from the 2024 Call within the field of Data-Driven Life Science

Cell and molecular biology

Proposal title	Main PI	Co-supervisor(s)
Charting cell differentiation in single-cell omics data via transcription-dynamics-informed optimal transport	Joakim Dahlin, KI	Johan Karlsson, KTH
Enabling variant-aware long read mapping for complex SV detection	Kristoffer Sahlin, SU	Adam Ameur, NGI/Uppsala Genome Centre
Merging and mining of image omics for discovery of early breast cancer progression cues	Ida-Maria  Sintorn, UU	Carina Strell Ingela Lanekoff, UU
Personalized Medication Strategies to Enhance Efficacy and Reduce Adverse Effects	Åsa Johansson, UU	Cemal Erdem Stefan Enroth, UmU
Quantitative predictions of protein – DNA interactions from high-throughput biophysical binding data	Emil Marklund, SU	Arne Elofsson, SU
Flow Matching for Managing Missingness in MALDI-MSI: Super Resolution and Completion of Single Cells in Brain Tissue Sections	Hossein Azizpour, KTH	Per Andrén, UU; Lukas Käll, KTH

Evolution and biodiversity

Proposal title	Main PI	Co-PI(s)
The Archaic within Us:  Functional consequences of  archaic sequences in modern human genomes	Maximilian Larena, UU	Mattias Jakobsson, UU; Carina Schlebusch, UU
AI-Based Multispecies Coalescent and Species Delimitation	Jens Lagergren, KTH	Christine Bacon, GU
Transcriptome-guided AI deconvolution of taxonomy (Traident)	Marc Friedländer, SU	Bastian Fromm, UiT The Arctic University of Norway
Discovering patterns in the evolution of codon usage	Ingemar André, LU	Sinisa Bjelic, Linnaeus University

Epidemiology and Biology of infection

Proposal title	Main PI	Co-PI(s)
Autoregressive probabilistic models of protein structure	Benjamin Murrell, KI	Gerald McInerney Daniel Sheward, KI
Data-driven approach to uncover the role of small cryptic plasmids in driving antibiotic resistance evolution	Helen Wang, UU	Luisa Hugerth, UU; Dan I. Andersson, UU

Precision Medicine and Diagnostics

Proposal title	Main PI	Co-PI(s)
Foundation models meet graph-based learning to advance spatial biology towards patient-specific cancer immunotherapy	Nataša Sladoje, UU	Patrick Micke, UU
Precision Medicine for Cardiometabolic Disease: Multi-Modal Analytics to leverage Disease Heterogeneity	Paul Franks, LU	Maria Gomez, LU
Deep learning from images and spatial omics data for precision immuno-oncology	Anna M Sandström Gerdtsson, LU	Catharina Hagerling, LU; Patrik Edén, LU; Victor Olariu, LU; Sara Ek, LU; Maria-Louise Elkjaer, Hamburg University
Causes and consequences of whole-body composition using imaging, genetic, proteomic and metabolomic data	Joel Kullberg, UU	Tove Fall, UU; Susanna Larsson, UU; Lars Lind, UU; Johan Öfverstedt, UU; Elin Lundström, UU
Charting Glioblastoma Invasion with Data-Driven Spatial Perturbation Models	Sven Nelander, UU	Rebecka Jörnsten, GU/Chalmers; Mats Nilsson, SU
Imaging the spatial risk of atherosclerosis – Understanding regional stability through data-driven multidimensional analysis	David Marlevi, KI	Ulf Hedin, KI; Ljubica Matic, KI
AI-based Analysis of Cleared Human Bone	Giovanni Volpe, GU	Andrei Chagin, Sahlgrenska Academy/GU

Approved Industrial PhD Projects in Data-driven Life Science

Proposal title	Main PI	Industry Co-PI(s)
Preventing Harmful Chemical Impacts: New AI-based strategies for improved human and environmental health	Erik Kristiansson, Chalmers	Jens Henriksson, Semcon Sweden AB
Deep learning modeling of spatial biology data for expression profile based drug repurposing	Erik Sonnhammer, SU	Dimitri Guala, Merck
Generative AI and data-driven design of lipid nanoparticles for targeted delivery	Maggie Holme, Chalmers	Martina Pannuzzo, AstraZeneca
Advanced Functional Embeddings for AI-Based Health Metrics and Explainable AI in Precision Medicine	Mika Gustafsson, LiU	Maria Lerm, PredictMe AB
Deconvoluting the molecular heterogeneity of drug effects and treatment response	Jochen Schwenk, KTH	Åsa Hedman, Pfizer
Data-Driven Metabolite and Site-of-Metabolism Prediction for Accelerated Drug Discovery	Rocío Mercado, Chalmers	Filip Miljković, AstraZeneca
AlphaFold Cytiva	Arne Elofsson, SU	Sarah McComas, Cytiva

The following PhD projects have been approved from the 2023 Call within the field of Data-Driven Life Science

Cell and molecular biology

Proposal title	Main PI	Co-PI(s)
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, KI; Carolina Wählby, 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	Co-PI(s)
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, University of Fribourg; Diana O. Fisher, University of Queensland; Royal Botanic Gardens, Kew; Alexandre Antonelli, GU; Jon Norberg, 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	Co-PI(s)
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, Chalmers	Joakim Larsson & Johan Bengtsson-Palme, GU/Chalmers
Developing methods for inferring transmission chains and disease outbreak surveillance in a hospital setting	Philip Gerlee, Chalmers	Jon Edman Wallér, GU

Precision Medicine and Diagnostics

Proposal title	Main PI	Co-PI(s)
Prediction of Single Cell Drug Response for Precision Cancer Medicine using Foundational Deep Learning Models	Kasper Karlsson, KI	Jens Lagergren, KTH; Avlant Nilsson, 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, GU; Björn Andersson, GU; Markus Schirmer, Harvard Medical Shool, US
A precision study of molecular health and aging in Swedish population cohorts	Sara Hägg, KI	Jochen Schwenk, KTH; Patrik Magnusson, KI
Network-based cancer precision medicine using proteogenomics	Janne Lehtiö, KI	Wojciech Chacholski, KTH; Avlant Nilsson, KI; Ioannis Siavelis, KI
Improving prostate cancer diagnostics and prognostication using artificial intelligence	Martin Eklund, KI	Kimmo Kartasalo, KI; Lars Egevad, KI
Deciphering Multiple Sclerosis: A Data-Intensive Approach to Unraveling Clinical and Molecular Complexities through Graph and Language Modeling	Ingrid Kockum, KI	Narsis Kiani, KI/Cambridge University; Ali Manouchehrinia, KI

Approved Industrial PhD Projects in Data-driven Life Science

Proposal title	Main PI	Co-PI(s)
Tailored Protein Panel Composition in Biomarker Discovery Using Concrete Autoencoders	Lukas Käll, KTH	Lina Hultin-Rosenberg, Olink Proteomics AB; Fredrik Edfors, KTH; Hossein Azizpour, KTH; Linn Fagerberg, Proteomics AB
Development and validation of AI-based histopathology phenotyping solutions to scale and accelerate breast cancer research	Mattias Rantalainen, KI	Stephanie Robertson, Stratipath AB; 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, Chalmers; Werngard Czechtizky, 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, UU; Johan Öfverstedt, UU; Elin Lundström, UU
Towards precision medicine in obesity with high cardiometabolic risks	Rashmi Prasad, LU	Sara Hansson, AstraZeneca

For questions please contact: ddls-rs@scilifelab.se