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
 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
 7. Nanomaterials: From Clay to Biomaterials
 8. Non-adiabatic chemical processes: chemistry beyond the Born–Oppenheimer approximation

2. Human Function and Environment

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

3. Life Science

 1. Automated and scalable predictive modeling in drug discovery with cloud computing, micro-services and Big Data frameworks
 2. Cancer Landscapes:
an interactive, global map of regulation in human cancer
 3. Computational Biology
 4. Development of eScience methods in drug discovery
 5. Large-scale analysis of live cells
 6. Translating long-read sequencing and metabolomics to clinical applications
 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
 2. Computational Design Optimization and Inverse Problems for Wave Propagation Problems
 3. Computational methods for two-phase flow
 4. Distributed data analysis – grid computing
 5. Efficient and Reliable HPC Algorithms for Matrix Computations in Applications
 6. Grid research
 7. Grid Middleware: Development and Support
 8. NoSQL Approach to Large Scale Analysis of Persisted Streams
 8. Parallelization of dynamic algorithms
 9.UMIT – modeling, simulations, computational methods, HPC software, IT infrastructures, and applications


One purpose of eSSENCE is to foster new collaborations between different
disciplines to solve problems of mutual interest. Examples of projects
with collaborations between different departments within the same
university are listed below

  1. Chemistry and Computer science – 1.2
  2. Medicine and Computer science – 2.1 2.2
  3. Medicine and Applied mathematics – 2.5 2.2
  4. Applied mathematics and Computer science –
  5. Archaeology and Computer science –
  6. Molecular biology and Computer science –
  7. Physics and Applied mathematics –
  8. Biology and Applied mathematics –
  9. Climate and Computer science –

Humanities and social sciences 

In the projects        , computing, mathematics and software development are used in the humanities and the social sciences to solve scientific problems. Progress in computational science is here important for new discoveries and insights in archeology, geography, linguistics, and psychology.

Data science

The amount of scientific data grows very rapidly and there is a need to
analyze large data sets to extract new knowledge from them. Methods are
developed for that in the projects                ,ranging from medicine and bioinformatics to astronomy.


Computational tools and software are developed and are open for everyone
to use in the projects             . The software solves problems in theoretical chemistry, biomedicine, demography, archaeology, pharmacy, cell biology, and high energy physics.

Industrial collaborations

Methods for simulation of industrial processes and optimal design are found in  . Large data sets are use for predictive modeling in pharmacy in . Sensors produce large data sets in industrial applications in . High-performance and cloud computing is made robust and efficient in collaboration with leading software companies in .

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