Department of Informatics and Chemistry

Study place:	Department of Informatics and Chemistry, FCT, VŠCHT Praha
Guaranteeing Departments:	Department of Informatics and Chemistry
Study Programme/Specialization:	Bioinformatics ( in Czech language )
Supervisor:	prof. Mgr. Daniel Svozil, Ph.D.
Expected Form of Study:	Combined
Expected Method of Funding:	Employment relationship possible

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This industrial PhD merges Cheminformatics, AI, and NMR to transform drug discovery. The candidate will refine AI|ffinity’s NMR-AI platform for virtual screening, hit discovery, and hit-to-lead optimization. Their focus includes: (1) enhancing 2D molecular representations using 1D NMR spectra to boost ligand-based virtual screening, (2) refining AI-driven structure-based hit-to-lead workflows harnessing 1D NMR restraints, and (3) innovating de novo design by integrating ligand epitope data from 1D NMR experiments. The candidate will also embed deep learning-powered screening and de novo generation into a reinforcement learning system, extracting ligand epitope information from 1D NMR data to identify novel bioactive molecules and to search commercial libraries. Promising compounds will be experimentally validated, driving iterative refinement and synthesis. The best leads advance toward in vitro assessment, and all newly developed computational methods will be integrated into AI|ffinity’s Drug Discovery platform.

Study place:	Department of Informatics and Chemistry, FCT, VŠCHT Praha
Guaranteeing Departments:	Department of Informatics and Chemistry
Study Programme/Specialization:	Bioinformatics ( in Czech language )
Supervisor:	doc. Ing. Filip Lankaš, Ph.D.
Expected Form of Study:	Combined
Expected Method of Funding:	Not funded

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Structure, conformational dynamics and mechanical properties of DNA and RNA molecules play a key role for the function of these molecules in living organisms and their knowledge is indispensable for a rational design of artificial nucleic acid nanostructures. Structural and mechanical properties of nucleic acids are determined by thei sequence of bases and are modulated by effects of the envirnment. Despite considerable research effort, this relation is still not fully understood. The goal of the work is to design suitable models of DNA and RNA structure and mechanics which may shed more light on these relations. In the case of double-helical DNA and RNA, we envisage models representing the molecule as an ensemble of interacting rigid bodies. The bodies will stand for individual bases and perhaps other parts of the molecule (phosphate groups, sugars), or will each encompass a group of these building blocks. The plan is to make a transition from harmonic models expressing the interaction energy between the bodies as a quadratic function of internal coordinates, to multistate models reflecting structural polymorphism of the molecules. A generalization of the models to non-helical structures will be considered as well. The model parameters will be deduced from all-atom molecular dynamics simulations of a large set of molecules.

Study place:	Department of Informatics and Chemistry, FCT, VŠCHT Praha
Guaranteeing Departments:	Department of Informatics and Chemistry
Study Programme/Specialization:	Drugs and Biomaterials ( in English language )
Supervisor:	Mgr. Jan Pačes, Ph.D.
Expected Form of Study:	Combined
Expected Method of Funding:	Not funded

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Biotelemetry is a modern and effective method for monitoring fish populations, with the key advantage of enabling the collection of valuable data on fish movement and behavior without the need for recapture at the end of the study. This method allows for the long-term observation of population dynamics; however, the resulting time series often contain gaps due to limited signal availability or other environmental factors. The objective of this study is to design and implement suitable extrapolation methods based on Markov processes to compensate for missing data and ensure more robust analyses. A crucial aspect will also be the identification of unobserved states within the fish population using Hidden Markov Models (HMM), which will help improve the understanding of population dynamics even in situations where direct observation is not possible. The findings of this study may contribute to a better understanding of ecological processes affecting fish populations while providing valuable tools for optimizing aquaculture practices and improving fisheries management strategies.

Institute of Macromolecular Chemistry of the CAS

Study place:	Institute of Macromolecular Chemistry of the CAS
Guaranteeing Departments:	Department of Informatics and Chemistry Institute of Macromolecular Chemistry of the CAS
Study Programme/Specialization:	Chemistry ( in English language )
Supervisor:	Ing. Zdeněk Starý, Ph.D.
Expected Form of Study:	Full-time
Expected Method of Funding:	Scholarship

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Nowadays new applications and processing technologies place new and bigger demands on polymeric materials. Materials for 3D printing or electrically conductive polymer composites can serve as typical examples. In most cases these systems have a heterogeneous phase structure, which influences the end-use properties of the final material to a large extent. The emergence of nanocomposite technology has provided a revolutionary new solution to wide range of technological aspects, not only provided novel functionalities, but also solved fundamental problems of polymer composites such as flammability and poor mechanical properties. The aim of the work is to develop novel highperformance polymer composites/ nanocomposites and discover the relationships between structure and properties of materials relevant for practical applications. Work activities include a synthesis of novel multifunctional nanomaterials, preparation of polymeric materials and chemical and structural investigations by means of different advanced characterization techniques. Furthermore, mechanical and flow behaviour of prepared materials will be studied in detail including flow instabilities analysis.

Institute of Organic Chemistry and Biochemistry of the CAS

Study place:	Institute of Organic Chemistry and Biochemistry of the CAS
Guaranteeing Departments:	Department of Informatics and Chemistry Institute of Organic Chemistry and Biochemistry of the CAS
Study Programme/Specialization:	Bioinformatics ( in English language )
Supervisor:	Mgr. Tomáš Pluskal, Ph.D.
Expected Form of Study:	Full-time
Expected Method of Funding:	Scholarship

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Our lab combines experimental (mass spectrometry, metabolomics and RNA-seq) and computational (bioinformatics and machine learning) approaches for the discovery of novel bioactive molecules derived from plants. The aim of this project will be the development of new computational methods for processing and interpreting small molecule mass spectrometry data, in particular for automated mass spectra interpretation, molecule annotation, and generation and visualization of molecular networks. Candidates for this position should be able to independently program in Java and Python.

Study place:	Institute of Organic Chemistry and Biochemistry of the CAS
Guaranteeing Departments:	Department of Informatics and Chemistry Institute of Organic Chemistry and Biochemistry of the CAS
Study Programme/Specialization:	Bioinformatics ( in English language )
Supervisor:	Mgr. Tomáš Pluskal, Ph.D.
Expected Form of Study:	Full-time
Expected Method of Funding:	Scholarship

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The goal of this project is to enable computational characterization and engineering of terpene synthases, an important class of biosynthetic enzymes responsible for creating the chemical scaffolds of the largest class of natural products, terpenoids. The project has three objectives: 1. Assembly of a comprehensive database describing the reaction mechanisms of terpene synthases characterized to date. 2. Development of a deep learning model using transformer neural networks for predicting the substrates, products, and reaction mechanisms of terpene synthases directly from their amino acid sequences. 3. Development of a generative algorithm for designing artificial terpene synthases with a desired function.

Institute of Physiology of the CAS

Study place:	Institute of Physiology of the CAS
Guaranteeing Departments:	Department of Informatics and Chemistry Institute of Physiology of the CAS
Study Programme/Specialization:
Also available in study programmes:	Bioinformatics ( in English language ), Bioinformatics ( in English language )
Supervisor:	Mgr. Tatyana Kobets, Ph.D.
Expected Form of Study:	Combined
Expected Method of Funding:	Not funded

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Blending clinical and basic research is crucial for advancing the understanding and treatment of cardiovascular diseases, including cardiac arrhythmia, hypertension, and other disorders, leading to heart failure. This PhD project focuses on developing computational pipelines for integrating gene expression, proteomics, and metabolomics data using R, Python, and advanced statistical methods. The aim is to create user-friendly, robust workflows that enable seamless analysis and interpretation of omics datasets in a clinical setting. Based on biopsies, human samples, and large and unique omics datasets, this project represents a collaboration between the scientific institute (IPHYS) and the clinical research centre (IKEM) in Prague. Funded by the CarDia consortium (https://cardia.ikem.cz/en/home ), this full-time position at IPHYS offers an opportunity to contribute to translational research with real-world clinical impact.

Study place:	Institute of Physiology of the CAS
Guaranteeing Departments:	Department of Informatics and Chemistry Institute of Physiology of the CAS
Study Programme/Specialization:
Also available in study programmes:	Bioinformatics ( in English language ), Bioinformatics ( in English language )
Supervisor:	RNDr. Ondřej Kuda, Ph.D.
Expected Form of Study:	Full-time
Expected Method of Funding:	Salary
Other expected Forms of Study / Methods of Funding:
Combined / Not funded ( in study programme - Bioinformatics ( in English language ) )

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This PhD project focuses on advancing the integration of metabolomics and lipidomics to unravel the regulation of complex biochemical networks and metabolic dynamics. The study leverages state-of-the-art data processing techniques, computational tools, and machine learning algorithms to extract actionable insights from large-scale fluxomics datasets. Python-based pipelines will be developed to standardize data preprocessing, feature extraction, and analysis while incorporating machine learning models for network clustering, classification, and predictive modeling of metabolic pathways. The project emphasizes cross-disciplinary approaches, blending expertise in biochemistry, computational biology, and data science to create robust tools for understanding metabolic systems. Outcomes are expected to contribute to personalized medicine, metabolic engineering, and systems biology, offering novel methodologies and software frameworks for the scientific community. The work will be conducted at the IPHYS CAS, where the metabolomics and proteomics service laboratory is located. The work is financially secured in terms of material and full time position. The prerequisites for success are knowledge of programming languages for working with data (Python), basic biochemistry (metabolites, pathways, cellular compartments), and an overview of omics disciplines.