Doktorské studium

Studijní obor Biomolekulární chemie

Název práce: Machine learning methods in nuclear magnetic resonance spectroscopy


Oficiální zadání:
The aim is to develop advanced methods for processing NMR spectroscopic data. NMR spectroscopy is a technique which exploits magnetic properties of atoms and molecules to determine their physical and chemical properties. In this particular case, the NMR spectroscopy is a part of a system for determining structure of proteins, where the latter finds many applications in medical research, drug design etc. NMR spectroscopy produces large amounts of data that need to be automatically processed. For example, one of the main problems is mapping of chemical shifts (peaks in NMR spectra) to particular atoms in a given protein. Solving such a problem may comprise statistical and machine learning methods as well as graph theoretic reasoning. Our plan is to use deep learning techniques but other learning methods may come to play as needed. This research will be done in collaboration with the research group of Konstantinos Tripsianes at Central European Institute of Technology (CEITEC). Candidate: We look for a highly motivated candidate with strong interest in this particular research topic who holds a degree in computer science/mathematics. We expect considerable experience in Python programming and at least basic knowledge of machine learning techniques.
doc. RNDr. Tomáš Brázdil, Ph.D. - Supervisor

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