Sort by
Refine Your Search
-
Category
-
Employer
-
Field
-
few years have focused on the generation of machine-learning force fields for biological molecules, such as SpookyNet or AI2BMD. These offer the accuracy of quantum-chemical methods with many orders
-
special light-switchable organic molecules that mimic the function of biological systems. These synthetic "molecular machines" will be composed of various photoswitches and molecular motors, with
-
-distance-energy data coming from Scanning Probe Microscopy on surfaces relevant for life sciences research and medicine, including curve classification tools based on machine learning, providing both
-
, metabolomics, RNA-seq) and computational (e.g., bioinformatics, molecular networking, machine learning) approaches to develop new workflows for the discovery and utilization of bioactive molecules derived from
-
, metabolomics, RNA-seq) and computational (e.g., bioinformatics, molecular networking, machine learning) approaches to develop new workflows for the discovery and utilization of bioactive molecules derived from
-
methods of detection, identification, classification and tracking of objects of different sizes, shapes and speeds of movement using elements of artificial intelligence and machine learning. • Research work
-
others) in development of molecular databases for machine learning and force field development. Field: Computational Chemistry and Physics, Biophysics University: University of Ostrava Location: Ostrava
-
of progressive methods of detection, identification, classification and tracking of objects of different sizes, shapes and speeds of movement using elements of artificial intelligence and machine learning