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Awarded Projects

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Collaborative Research Programme
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2018
2019
2020
2021
2022
2023
2023
APEX – Artificial Intelligence for Policy Excellence in the Climate Crisis
Francisco Pereira, Technical University of Denmark
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Unravelling Immunosenescence from Single Cell and Spatial data
Nicolai Birkbak, Aarhus University
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Charting territories: Refining case-cohort estimation and utilize register-based factors in genetic analyses
Liselotte Vogdrup Petersen, Aarhus University
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Early detection of epidemics in livestock and wildlife using deep learning
David Duchene Garzon, University of Copenhagen
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Specific gene regulation in space and time through oscillations and liquid-liquid phase separation
Mathias Spliid Heltberg, University of Copenhagen
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Mechanoreciprocity in in-silico biological matter
Kristian Thijssen, University of Copenhagen
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Data-adaptive targeted machine learning methods for analyzing dynamic treatment effects in observational healthcare data
Helene Charlotte Wiese Rytgaard, University of Copenhagen
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New computational approaches for understanding the emergence of social network structures
Josefine Bohr Brask, University of Copenhagen
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SE3D: Synthetic health data: ethical development and deployment via deep learning approaches
Martin Bøgsted – Professor, Center for Clinical Data Science, Aalborg Universitet
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Tensor Networks for Data Science: Stability, Efficiency and Explainability at Scale
Morten Mørup –Professor, Section for Cognitive Systems, DTU Compute, Danmarks Tekniske Universitet
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Unravelling shared disease mechanisms of ADHD and obesity by large-scale multidimensional data analyses
Ditte Demontis –Professor, Department of Biomedicine, Aarhus Universitet
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Deep Fetal Development: AI for Fetal Development Analysis and Impact on Future Health of Mother and Child
Mads Nielsen –Professor, Department of Computer Science, Københavns Universitet
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2022
Pioneer Centre for AI
Serge Belongie, University of Copenhagen
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ALFADIAB – Algorithmic Fairness in Diabetes Prediction
Tibor V. Varga, University of Copenhagen
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Integration of longitudinal multimodal data in clinical risk prediction using deep learning
Adam Hulman, Steno Diabetes Center Aarhus
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Optimizing medical decision making: advancing and refining estimation and evaluation of personalized treatment rules
Erin Gabriel, University of Copenhagen
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Bayesian neural networks for molecular discovery
Mikkel Schmidt, Technical University of Denmark
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A simulation and inference toolbox for spatio-temporal genome evolution
Fernando Racimo, University of Copenhagen
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GolgiNet: Data science to take glycomics in silico and beyond
Hiren Joshi, University of Copenhagen
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DEVELOP: Using data science to estimate overdiagnosis and mortality reduction in cancer screening programs
Sisse Njor, Randers Regional Hospital
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Cognitive spaces – Next generation explainability
Lars Kai Hansen, Technical University of Denmark
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AgroEco-HPM: A high-performance data-driven agroecosystem modelling platform for developing agricultural systems with minimum environmental impact
Sune Darkner – Associate Professor, Department of Computer Science, Københavns Universitet
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Biomedical Data Infrastructure (BDI) for health research: National Neurophysiologic Database (NND)
Poul Jørgen Jennum – Professor, Department of Clinical Neurophysiology, Rigshospitalet
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A high-performance computing infrastructure for data-driven research on sustainable energy materials
Kristian Sommer Thygesen – Professor, Department of Physics, Danmarks Tekniske Universitet
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Data Science Research Infrastructure In Radiotherapy
Cai Grau – Professor, Department of Clinical Medicine, Aarhus Universitet
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MOPITAS – Multi-omics profiling in time and space
Richard Röttger – Associate Professor, Department of Mathematics and Computer Science, Syddansk Universitet
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Reading the Reader
Per Bækgaard – Associate Professor, Department of Applied Mathematics and Computer Science (DTU-Compute), DTU
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CAZAI: CAZyme Specificity Prediction Using AI
Bernard Henrissat – Professor, Department of Biotechnology and Biomedicine, DTU
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2021
The Danish Data Science Academy
Lars Kai Hansen, Technical University of Denmark
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Providentia: Privacy-driven Trust in Algorithms
Rasmus Pagh, University of Copenhagen
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Novel methods to model mutational processes in germline and cancer
Søren Besenbacher, Aarhus University
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First principal models, neural networks and functional graphical models for Defining metabolic capacity as a Tool for Personalized nutrition (FOODTOP)
Morten Arendt Rasmussen, University of Copenhagen
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Enhancing statistical methodology for toxicophenomics: High-throughput and high-dimensional data for ecotoxicological risk assessment
Signe M. Jensen, University of Copenhagen
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Islet Cartography – Multi-layered mapping of islets of Langerhans in health and disease
Jesper Madsen, University of Southern Denmark
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Efficient, high-resolution approaches for integrative sequencing analysis of complex diseases
Shilpa Garg, University of Copenhagen
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CausalBiome: Developing a unified statistical framework for analyzing microbiome data
Niklas Pfister, University of Copenhagen
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QUAITOM – The Infrastructure for QUantitative AI-based TOMography
Anders Dahl, Technical University of Denmark
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Risk-assessment of Vector-borne Diseases Based on Deep Learning and Remote Sensing
Jakob Brandtberg Knudsen, The Royal Danish Academy of Fine Arts
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DIRECTION – Data science driven leaf architecture optimization
Staffan Persson, University of Copenhagen
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CRISPRnet: Deep learning and data-driven CRISPR design for network-based multiplexed targeting
Jan Gorodkin, University of Copenhagen
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A framework for an open and scalable infrastructure for health data exemplified by the DD2 initiative
Annelli Sandbæk, Aarhus University Hospital
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Mutational processes in spermatogenesis and their consequences for human health
Mikkel Heide Schierup
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Personalized medicine infrastructure using the open-source OMOP common data model including an Electronic Health Record interface
Ismail Gögenur, Zealand University Hospital
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Novo Nordisk Foundation Center For Genomic Mechanisms of Disease at Broad Institute
Kasper Lage, Broad Institute
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2020
Machine learning methods for data-driven discovery of antibiotic resistance plasmid dissemination and evolution
Søren Sørensen, University of Copenhagen
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Center for Basic Machine Learning Research in Life Science
Ole Winther, University of Copenhagen
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National Health Data Science Sandbox for Training and Research
Anders Krogh, University of Copenhagen
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The OpenNeuroPET Archive – A Molecular Neuroimaging Archive
Gitte Moos Knudsen, Rigshospitalet
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Data science approaches to study epidemiological and genetic underpinnings of hypothyroidism to pave the way for precision medicine
Tugce Karaderi, University of Copenhagen
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Differentiable Physical Models for Data Analysis in Biology
Julius Kirkegaard, University of Copenhagen
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Leveraging artificial intelligence for pancreatic cancer diagnosis, treatment planning and treatment outcome prediction
Bulat Ibragimov, University of Copenhagen
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Ancient Genomes Reconstruction
Gabriel Renaud, Technical University of Denmark
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Identifying Heterogeneous Treatment Effects with Machine Learning
Anders Hviid, Statens Serum Institute
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Scalable Optimization for Data Science
Martin Andersen, Technical University of Denmark
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CLUE: Causal Learning with Unstructured Events
Niels Richard Hansen, University of Copenhagen
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Statistical inference for coupled stochastic processes with multiple timescales and changing environments
Susanne Ditlevsen, University of Copenhagen
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The Danish Multigeneration Register
Anne-Sofie Jensen, Danish National Archives
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2019
Synthetic Health and Research Data (SHARED)
Henning Langberg, University of Copenhagen
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2018
Big Data Centre for Environment and Health
Clive Sabel, Aarhus University
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Big life-course data analytics for understanding disease initiation and progression in diabetes and its complications
Søren Brunak, University of Copenhagen
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Harnessing the Power of Big Data to Address the Societal Challenge of Aging
Rudi Westendorp, University of Copenhagen
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