Sort by
Refine Your Search
-
Listed
-
Country
- United States
- United Kingdom
- Germany
- Sweden
- France
- Spain
- Czech
- Poland
- Canada
- Austria
- Belgium
- China
- Italy
- Denmark
- Australia
- Portugal
- India
- Japan
- Singapore
- Lithuania
- Switzerland
- United Arab Emirates
- Macau
- Netherlands
- Barbados
- Croatia
- Europe
- Finland
- Greece
- Hong Kong
- Israel
- Malta
- Mexico
- Norway
- 24 more »
- « less
-
Field
-
Description In this project, we develop machine learning models for prediction of optical properties of chiral molecules based on DFT/CCSD data which we calculate ourselves. We include derivative information by
-
of epigenetic marks. These properties are actively regulated by molecular processes such as DNA-protein co-condensation and loop extrusion. In this project we aim to understand how emergent properties
-
/InstituteBiologie du Développement et Cellules SouchesCountryFranceCityPARIS 15Geofield Contact City PARIS 15 Website https://research.pasteur.fr/en/team/genetic-molecular-and-cellular-bases-of-development/ STATUS
-
. Their molecular behavior has profound effects on soil hydraulic properties and biome stability. However, despite advances in continuum modeling of soil and plant-water systems, the molecular-scale mechanisms
-
science to applied research using plant experimental model systems, crops and farm animals) make extensive use of genomic technologies and large sets of genetic and genomic data (https://www.cragenomica.es
-
formed during metal extraction in glycine leaching processes. This work will advance both the fundamental chemical understanding of GLT and the application of molecular modelling to hydrometallurgy and
-
to the development and use of new in vitro model systems for neurodegenerative diseases and brain cancer. The group is led by Henrik Ahlenius and includes 3 postdocs, an associate researcher, and a number of master’s
-
and implement multimodal retrieval with re-rankers for robust profile selection. Design and train advanced AI models for digital twin: 3D model learning, prediction models from imaging and molecular
-
cells. Experience in some of the following techniques: flow cytometry, bioimaging, 3D in vitro models, organoids, molecular biology/DNA cloning. Capacity to develop, independently, projects in immune
-
Single-cell high-throughput sequencing technologies generate unprecedented volumes of molecular data at cellular resolution, opening new avenues for the application of machine learning