Nettsider med emneord «Active learning»
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.
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.
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.
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.
UiO:RealArt will use artificial world data to study the real-world problem of safe medication use in pregnancy.
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 – 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.
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.
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.
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).