Research activities

The aim of this research is to develop a high-fidelity digital twin model of a spacecraft (DTS) to efficiently analyse, test, and optimize novel concepts and operational services for future Earth Observation (EO) missions based on NewSpace satellites.

We use quantum mechanical theory (density functional theory) and develop statistical methods such as Monte Carlo techniques, molecular dynamics, thermodynamic integration, genetic algorithms in conjunction with machine learning to understand more about deep earth processes and core-mantle interactions.

Implement anisotropic viscosity (AV) calculations into a geodynamic code ASPECT, to understand its role in a variety of geodynamic processes.

Recently maturing software for stellar modeling and evolution, such as MESA, have been applied to constraining variations of the gravitational constant and exploring such a variation¡¯s ability to account for the Hubble tension between the late- and early- time universe by biasing the local distance ladder.

We live in a Universe composed of a large variety of chemical elements. The element distribution we observe, and in particular the diverse abundances of atomic nuclei, tells a fascinating story of nucleosynthesis events that have taken place throughout the 13.7-billion-year-long history starting with the Big Bang.

Bayesian methods have recently regained a significant amount of attention in the machine community due to the development of scalable approximate Bayesian inference techniques. There are several advantages of using Bayesian approach: Parameter and prediction uncertainty become easily available, facilitating rigid statistical analysis. Furthermore, prior knowledge can be incorporated.

BigInsight produces innovative solutions for key data-driven challenges facing our consortium of private, public and research partners, by developing original statistical and machine learning methodologies.

While Machine Learning algorithms have in recent years seen great progress, there are still scenarios in which they fail to be as robust and flexible as animals and humans.

The selective methane oxidation to methanol represents a critical challenge due to the difficulty in activating the strong C?H bond of methane, preventing methanol oxidation. In this project, single-site catalysis based on metal organic frameworks (MOFs) will be developed taking natural enzymes based on copper as inspiration.

The conversion of CO2 to fuels using renewable energies involves catalysts that require further optimization for their large-scale implementation. In this project, DFT methods and microkinetic models are used to gain quantitative insight into the mechanism of these reactions and the steps governing catalytic activity and selectivity.

The Centre for Earth Evolution and Dynamics (CEED) is a Centre of Excellence dedicated to research of fundamental importance to the understanding of our planet.

Gold complexes are synthesized for catalytic applications. The structures, bonding, and reactivities of the complexes are investigated using computational methods, primarily Density Functional Theory (DFT).

G-protein coupled receptor (GPCRs) form the largest superfamily of membrane proteins in human. 34% of the marketed small molecule drugs bind to GPCRs. Tens of millions of compounds are commercially available for screening against GPCRs in experimental setting, which is impractical for academia and industry.

Colorectal cancer (CRC) symptoms are unspecific ¨C often emerging when the disease is no longer curable. Screening reduces CRC mortality, but current screening tests need improvement to be more accurate and less costly and invasive. The overall aim of the CRCbiome study is to discover gut microbiota biomarkers for colorectal cancer screening.

The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice/water balance for permafrost and glaciers. The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming.

For organometallic complexes to be useful as photosensitizers, it is important to know how the electron densities in the excited states are distributed in the molecular structures. In order to gain detailed insight into the electronic structures of the complexes, computational investigations were performed.

The main objective of this work is to improve the utility of new small satellites for Earth Observation (EO), by researching machine learning techniques to obtain improved and useful detection, classification, and identification capabilities from space.

EBP-Nor will sequence and catalogue all eukaryotic species occurring in Norway, an estimated 45,000 species, to contribute to the global effort. Graph-based algorithms in assemblers and scaffolders are essential for this work.

The aim of the EMISSA project is to use data obtained with the Atacama Large Millimeter/sub-millimeter Array (ALMA) in the Chilean Andes for a re-evaluation of the activity of stars by means of a comparative solar-stellar study with the Sun serving as a fundamental reference.

Modern science usually provides both copious amounts of data and complicated models for the part of reality it is trying to describe. Often there is even so much data, and the models so complicated, that it becomes difficult to make full use of the data in deciding which models best describe the world around us, and finding their properties. The main goal of the GAMBIT project is to develop a software tool to help physicists do just that.

The main objective of this project is to provide a geoscientific solution for increasing the renewable (geothermal) energy production in northwest Romania. This will lead to a decrease in actual CO2 emissions generated by electrical and thermal energy production using fossil fuel.

The aim of this project is to determine the effectiveness of antidepressant treatment in pregnant and postpartum women, as well as the longer-term metabolic safety of these drugs in pregnancy on the offspring.

In HIDDEN we employ atomistic simulations (i) to establish the presence or not of a hidden geochemical reservoir in the deep mantle that can store noble gases, (ii) to calculate the permeability of the core-mantle boundary throughout geological time with respect to noble gases, (iii) to determine the exchanges of noble gases between the mantle and the core during the core formation, and (iv) to give estimates of fluxes of noble gases through the Earth¡¯s mantle throughout the geological time.

AI, statistical models and machine learning methods can often be seen as black boxes to those who construct the model and/or to those who use or are exposed to the methods.

The mission of the EDHEN project is to provide a new paradigm for the analysis of health data in Europe by building a large-scale, federated network of data partners across Europe.

This project aims to understand the origin and formation of small-scale magnetic reconnection events in the lower solar atmosphere and explore their role in the energy and mass transport from the lower to the upper solar atmosphere.

LATICE aims to advance the knowledge base concerning land atmosphere interactions through improved model representation of snow, permafrost, hydrology and large-scale vegetation processes representative of high latitudes, including; 1) New ground observations (gap filling using ML); 2) Land surface model parametrization using data science methods; 3) Seasonal snow cover dynamics using data assimilation and Earth observations (e.g. satellite data, drones).

Recent advances in ultrashort laser technology allow us to probe and potentially control molecules and chemical reactions at the level of electrons.

A six-year project with the goal to develop and use machine learning to improve the way social scientists can answer classic as well as emerging questions in economics that require the use of large datasets.

We develop and apply methods based on machine learning for chemistry and materials science. At the method level, our focus is on data (datasets computed with quantum mechanics methods), representations (graphs based on electronic structure theory), and models (graph neural networks and boosted trees).

The adaptive immune system records all past and ongoing battles with disease and infection in the form of immune memory, stored in the form of DNA of immune receptors of adaptive immune cells. However, deciphering these signals is a grand challenge of immunology, requiring sophisticated machine learning.

The MAGPIE project seeks to develop new constraints on uplift patterns in and around Greenland associated with past and present ice melting. For this, we are collecting magnetotelluric (MT) data from Greenland¡¯s interior, which we then use to constrain viscosity variations beneath Greenland.

In MASSIVE, the project team aims at improving glacier mapping and surface glacier mass balance estimation techniques with the help of machine learning, especially deep learning. We will develop the methodology for glaciers in Norway, Svalbard, the European Alps and the Himalayas and then expand it to regions with different glacier characteristics.

The dynamics of Chemical Reaction Networks (CRN), which are typically embedded within Mass and Energy Transport Phenomena such as diffusion or advection, govern the performance of innumerable industrial technologies.

Economic models used for forecasting and to aid policy decisions have been estimated by the use of data from before the Covid-19 pandemic and the ensuing lockdowns, drop in economic activity and surge in unemployment. An important question for model developers and users is therefore how the empirical relationships that represented normal behavior of firms and households before Covid-19 have been affected by the pandemic and by the policy responses.

The recent summer drought events in Europe and their associated devastating wildfires highlight the importance of understanding and predicting such extreme events and their impact.

In a wide range of applications, monitoring data streams for faults or changes in behavior (called anomalies) is of great importance.

This project analyzes written parliamentary questions in the Norwegian parliament (Stortinget) from 1998 to the present.

PSI (Political Science Infrastructure) is an ambitious infrastructure project within social science that will provide researchers nationally and globally with access to new and unexplored data sources to study political processes in fine-grained detail.

In Permafrost4Life, we investigate how permafrost is entangled with ecosystems and the traditional herder lifestyle important for the Mongolian society.

Digital twins are necessary for the successful digitalization of oil and gas field operations. Unfortunately, they are poorly understood and hyped.

The COVID-19 pandemic resulted in an unprecedented need for pharmacoepidemiological studies related to infectious disease, mental health, medication use, and vaccination.

Exploring the fundamental constituents of the Universe physicists are faced with very serious calculational bottlenecks. To compare new physics models to data we need to perform very computationally expensive calculations in quantum field theory (QFT).

POLARIS will investigate how the circum-Arctic region has changed over deep geological time. The main aim is to build a digital Earth model back to the Devonian (420 Million years) with a focus on plate tectonics and whole mantle convection (geodynamic processes).

PIRC targets a psychology-inspired computing breakthrough through research combining insight from cognitive psychology with computational intelligence to build models that forecast future events and respond dynamically.

Devices, like smart-watches, that can collect health data from "everybody" all the time, and machine learning (ML) to analyze this data will strongly impact future health solutions.

A primary goal of the Oman Drilling Project¡¯s Multi-Borehole Observatory has been to understand geo-chemical/physical changes in the rock fluid environment that can be used to store human generated CO2. These boreholes have produced dozens of terabytes of seismic, hydrologic, geologic, and rock mechanics data.

Greenhouse gas seepage into the oceans is a major environmental challenge.

Snow in the mountains is a source for drinking water, hydropower, irrigation, but can also cause floods and geohazards. There are currently no efficient methods to measure depth of snow in mountains and remote areas.

The computational challenge in modeling the Sun is both in simplifying the complex physics without losing the main properties and in treating a large enough volume to encompass the large structures with enough resolution to capture the dynamics of the system.

SPATUS is a Thematic Research Group funded by UiO:Energy.

For maritime safety surveillance we develop new approaches based on the availability of large arrays of sensors, which monitor condition and performance of vessels, machinery, or power systems.

The objective of StREP is to provide a comprehensive and unified account of the decision to request public votes and the consequences thereof for observed behaviour.

Accurate mapping of surface greenhouse gas fluxes is necessary for the validation and calibration of climate models. In this project, we develop a novel framework using drone observations and machine learning to estimate greenhouse gas fluxes at a regional scale.

The spatial configuration of continents and its temporal evolution exert a fundamental control on Earth¡¯s evolution. Before 130 Ma, plate motions can only be quantified through the study of paleomagnetism, however, individual paleomagnetic data cannot constrain longitude.

The standard model of cosmology has this last decade been increasingly challenged by observational tensions that might result eventually in the preference of some alternative model. But there is a large zoo of models to consider.

The physical reason for the observed acceleration of the Universe is one of the most important mysteries in cosmology, and arguably generally in fundamental physics.

Observational and theoretical evidence suggest that bathymetry exerts an enormous influence on ocean currents. The proposed work seeks to bridge the gap between oceanographic theory, observations and climate models by utilizing idealized, nonlinear numerical models to study how diverse phenomena evolve over bathymetry.

Heterogeneous catalysis is a key enabling technology for the green transition. Industrial catalysts are always shaped into millimeter-sized catalyst objects.

UiO:RealArt will use artificial world data to study the real-world problem of safe medication use in pregnancy.

The project consists in investigating the non-linear structure formation within the framework of dark energy, dark matter and modified gravity theories using N-body and hydrodynamic numerical simulations.

The goal of the project is to co-develop technology and proposals for regulatory measures to reduce vulnerabilities regarding robotics.

How does the Sun work? Why does it possess a magnetic cycle, dark spots and a dynamic hot atmosphere? These are questions that remain mostly unanswered. In the "Whole Sun" project, we aim at tackling these key questions as a coherent whole for the first time.