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

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