DDLS Postdoc Program

Introduction

Welcome to the DDLS Research School Postdoc Program, part of Sweden’s long-term investment in data-driven life science. The program offers postdoctoral researchers the opportunity to pursue independent, data-driven research projects in close collaboration with strong academic and industrial environments across Sweden.

We invite postdoctoral candidates, along with prospective academic and/or industrial supervisors, to apply to the current call for DDLS postdoctoral fellowships. The call outlines the program scope, eligibility criteria, evaluation process, and key dates, and serves as the formal entry point to the program

Fellows become members of the DDLS Research School and take part in a national framework for training, networking, and career development.

Approved Postdoc Projects in Data-driven Life Science 2026 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

Academic projects

Proposal Title	Postdoctoral Researcher
Deep learning-enhanced 3D spatial transcriptomics pipeline for mapping the glioblastoma vascular niche across therapy stages	Johannes Wirth, UU
Explainable Deep Learning for Structural Variant-Aware Genome-Wide Association Studies	Marcela Alvarenga, SU
AI-Based Computational Pathology for Breast Cancer Risk-Stratification Using a Mixture of Expert (MoE) Approach	Sayna Rotbei, KI
APEX-CHO: AI for Predictive Expression in CHO cells	Danish Anwer, Chalmers
ART-T: AI-driven Regulatory Network Transformation of T-cells – A Foundation Model for Predicting T-cell States and Engineering Therapeutic Responses	Akshayata Naidu, KI
Cross-Modal Knowledge Distillation with Computational Optics for Phenotypic Antibiotic Susceptibility Testing in Tuberculosis	Ali Hamza, UU
The Goldilocks Principle of Island Floras	Laura Schat, NRM
Decoding protease dynamics via deep learning to enable antiviral targeting	Carolyn, N Ashley, KI
Physics-Informed Generative Modeling of Protein Dynamics	Saurov Hazarika, SU
Spectrum and origins of chromosomal abnormalities in pregnancy loss and their interplay with genetic variation	Yan Zhao, GU
From Satellites to Seabed: A Data-Driven Framework for Coastal Ecosystem Lightscapes	Rubina Davtyan, UMU
Decoding Condensate Formation: Generative AI for Disordered Protein Dynamics and Therapeutic Targeting	Thomas R Sisk, Chalmers
Deep learning from multiplex immunofluorescence tissue images for precision immuno-oncology	Elham Akbari, LU
Data-Driven Reconstruction and Functional Decoding of GPR183 Mutational Landscapes	Patryk Adam Wesołowski, KI
An Integrative Data-Driven Framework for Studying Human Ligand-Gated Ion Channels Structure and Dynamics	Yao Wei, SU

Industry projects

Proposal Title	Postdoctoral Researcher
Learning a Generalizable and Explainable Pulmonary Health Score for Early Detection and Stratification of Lung Disease	David Martínez-Enguita, PredictMe AB
Stratifying patients by tau burden using synthetic tau biomarkers	Charles Chen, Centile Bioscience Inc.
Dynamic Radiotranscriptomic Endotyping in Bronchiectasis: Integrating Longitudinal CT and Whole-Blood Transcriptomics	Zhuoheng Li, Chiesi Pharma AB
Unveiling the Silent Majority of Chemical Data for Precision Therapeutics	Ali Amirahmadi, AstraZeneca
Efficient Training of Multimodal Foundation Models via Data Selection and Single-Modality Adaptors for Phenotypic Drug Discovery	Benjamin Midtvedt, IFLAI AB
Multiplexed Spatial Protein Interactions – A Data-Driven Framework for Interpreting Cellular States and Predicting Drug Response	Di Peng, Navinci
Enabling Edge-Efficient Foundation Models for Low-Resource Microscopy	Peter Ward, Enaiblers AB

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