Nettsider med emneord «Databases»
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.
Digital twins are necessary for the successful digitalization of oil and gas field operations. Unfortunately, they are poorly understood and hyped.
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.
UiO:RealArt will use artificial world data to study the real-world problem of safe medication use in pregnancy.
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.
The COVID-19 pandemic resulted in an unprecedented need for pharmacoepidemiological studies related to infectious disease, mental health, medication use, and vaccination.
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.
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.
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.
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 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.