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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
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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
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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
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Brno University of Technology, Central European Institute of Technology | Czech | about 2 months ago
-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
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Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences | Czech | about 2 months ago
, 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
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Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences | Czech | about 2 months ago
, 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
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Institute of Organic Chemistry and Biochemistry, Czech Academy of Sciences | Czech | about 2 months ago
analytical and physical chemistry. Online registration deadline: 3 March 2025 Interview Day: 20 March 2025 Learn more and apply at www.uochb.cz/en/call-for-phd-applications . Where to apply Website http
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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
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chemistry and accurate machine-learned oligopeptide potentials with applications in structural and evolutionary biology. First, we will develop a new computational tool, denoted as QMLFold. This would allow
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developing new methods. These include machine-learning based approaches to identify deoxyribozyme variants with new phenotypes. University: Faculty of Science, Charles University Group: Edward Curtis Group