Focus Areas & Projects

eSSENCE research focuses on four main areas: Materials Science, Human Function and Environment, Life Science and Generic e-Science Methods and Tools. Within each focus area research projects are funded on the basis of scientific quality, and particular support is given to collaboration projects with potential of opening up for novel applications.
Follow the links below for a detailed description of 40 research projects supported by eSSENCE.

1. Materials Science

 1. 4D-QMPM – 4D data processing to quantify the mechanics of porous media
 2. A multiscale modelling platform for Materials Chemistry
       Collaboration between Chemistry and Computer science departments at Uppsala University
 3. A unified theory of the rare-earth elements
 4. Chemistry of complex materials
 5. First-principles modelling of the L-edge X-Ray Absorption Spectroscopy of Transition Metal Oxides and Organic Molecules with Transition Metals Centers
 6. Multiscale modeling of Magnetization Dynamics
      Collaboration between Physics and Applied mathematics departments at Uppsala University
 7. Nanomaterials: From Clay to Biomaterials
 8. Non-adiabatic chemical processes: chemistry beyond the Born–Oppenheimer approximation
       Computational tools and software development

2. Human Function and Environment

 1. AAOT: Algorithms and Applications for Organ Transplantation
       Collaboration between Medicine, Computer science, and Applied mathematics departments at Lund University
        Large data set analysis methods development
 2. Data Management and Workflow Improvements In Biomedical Imaging
       Collaboration between Medicine and Computer science departments at Lund University
        Large data set analysis methods development
       Computational tools and software development
 3. Deep Learning for Natural Language Processing
       Computing, mathematics and software development
 4. Development of a regional CO2 fluxes data assimilation system
      Collaboration between Climate and Computer science departments at Lund University
        Large data set analysis methods development
 5. Improving intrapartum surveillance using a pattern recognition and machine learning approach
      Collaboration between Medicine and Applied mathematics departments at Lund University
        Large data set analysis methods development
 6. Information service for historical demographic and geographic information
        Computing, mathematics and software development
        Computational tools and software development
 7. Introducing the micro-level geographic context in historical demographic research
        Computing, mathematics and software development
 8. Linguistics and visual information
        Computing, mathematics and software development
 9. Semantic and visual processing
      Collaboration between Computer science and Applied mathematics departments at Lund University
 10. Statistical Machine Translation
        Computing, mathematics and software development
 11. Tailored Time-Frequency Features for Robust Classification of Electrophysiological Correlates of Human Memory Retrieval
        Computing, mathematics and software development
        Large data set analysis methods development
 12. The development of processing and assessment tools for event detection in eye movement data
        Computing, mathematics and software development
        Large data set analysis methods development
 13. Using High Performance Computing Resources for the Record and Analysis of Cultural Heritage Sites
      Collaboration between Archaeology and Computer science departments at Lund University
      Computing, mathematics and software development
      Large data set analysis methods development
      Computational tools and software development
 14. Classification tool for bird singing
      Collaboration between Biology and Applied mathematics departments at Uppsala University
        Large data set analysis methods development
 15. Computational Financial Statistics
 16. eStarSpec: using eScience to map the Milky Way
        Large data set analysis methods development
       Computational tools and software development

3. Life Science

 1. Automated and scalable predictive modeling in drug discovery with cloud computing, micro-services and Big Data frameworks
        Large data set analysis methods development
       Computational tools and software development
 2. Cancer Landscapes: an interactive, global map of regulation in human cancer
        Large data set analysis methods development
 3. Computational Biology
 4. Development of eScience methods in drug discovery
        Large data set analysis methods development
 5. Large-scale analysis of live cells
      Collaboration between Molecular biology and Computer science departments at Uppsala University
        Large data set analysis methods development
       Computational tools and software development
 6. Translating long-read sequencing and metabolomics to clinical applications
        Large data set analysis methods development
       Computational tools and software development
 7. Monte Carlo approach to proteins in cellular environments
 8. Virtual Chemistry

4. Generic e-Science Methods and Tools

 1. Autonomous Resource Management for Robust, Efficient, and High-Performance & Cloud Computing
      Large data sets for predictive modeling in pharmacy development
 2. Computational Design Optimization and Inverse Problems for Wave Propagation Problems
        Methods for simulation of industrial processes development
 3. Computational methods for two-phase flow
 4. Distributed data analysis – grid computing
        Large data set analysis methods development
       Computational tools and software development
 5. Efficient and Reliable HPC Algorithms for Matrix Computations in Applications
       Computational tools and software development
 6. Grid research
       Computational tools and software development
 7. Grid Middleware: Development and Support
       Computational tools and software development
 8. NoSQL Approach to Large Scale Analysis of Persisted Streams
       Industrial applications development for large data sets from sensors
 8. Parallelization of dynamic algorithms
 9. UMIT – modeling, simulations, computational methods, HPC software, IT infrastructures, and applications
       Computational tools and software development
       Methods for simulation of industrial processes development
       High performance and cloud computing in collaboration with leading software companies