|
Programme Details
| Study Language
|
Czech |
|
Standard study length
|
4 years |
| Form of study
|
combined
,
full-time
|
| Guarantor
|
prof. Mgr. Daniel Svozil, Ph.D.
|
| Place of study
|
Praha |
| Capacity
|
8 students |
| Programme code (national)
|
P0588D030009 |
| Programme Code (internal)
|
D107
|
| Number of Ph.D. topics
|
8 |
Ph.D. topics for study year 2025/26
Computational Biology for Integrative Omics in Clinical Research
|
Study place:
|
Institute of Physiology of the CAS
|
| Guaranteeing Departments: |
Department of Informatics and Chemistry
Institute of Physiology of the CAS
|
|
Also available in study programmes:
|
Bioinformatics (
in English language
),
Bioinformatics (
in Czech language
),
Bioinformatics (
in English language
)
|
| Supervisor: |
Mgr. Tatyana Kobets, 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 programmes -
Bioinformatics (
in English language
),
Bioinformatics (
in Czech language
) )
|
Annotation
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.
Integrated approaches for metabolomics and lipidomics - data-driven insight using machine learning and biochemical networks
|
Study place:
|
Institute of Physiology of the CAS
|
| Guaranteeing Departments: |
Department of Informatics and Chemistry
Institute of Physiology of the CAS
|
|
Also available in study programmes:
|
Bioinformatics (
in Czech language
),
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 programmes -
Bioinformatics (
in English language
),
Bioinformatics (
in Czech language
) )
|
Annotation
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.
Modelling the catalytic mechanisms of terpene synthases using deep learning
|
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
|
|
Also available in study programmes:
|
Bioinformatics (
in English language
)
|
| Supervisor: |
Mgr. Tomáš Pluskal, Ph.D.
|
| Expected Form of Study: |
Full-time |
| Expected Method of Funding: |
Scholarship |
| |
Annotation
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.
Modelling the structure, dynamics, and mechanical properties of nucleic acids
|
Study place:
|
Department of Informatics and Chemistry, FCT, VŠCHT Praha
|
| Guaranteeing Departments: |
Department of Informatics and Chemistry
|
| Supervisor: |
doc. Ing. Filip Lankaš, Ph.D.
|
| Expected Form of Study: |
Combined |
| Expected Method of Funding: |
Not funded |
| |
Annotation
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.
Advanced drug design using artificial intelligence and nuclear magnetic resonance
|
Study place:
|
Department of Informatics and Chemistry, FCT, VŠCHT Praha
|
| Guaranteeing Departments: |
Department of Informatics and Chemistry
|
|
Also available in study programmes:
|
Drugs and Biomaterials (FCT) (
in English language
)
|
| Supervisor: |
prof. Mgr. Daniel Svozil, Ph.D.
|
| Expected Form of Study: |
Combined |
| Expected Method of Funding: |
Employment relationship possible |
| |
|
Other expected Forms of Study / Methods of Funding:
|
|
Combined / Not funded ( in study programme -
Drugs and Biomaterials (FCT) (
in English language
) )
|
Annotation
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.
Machine Learning for Protein Structure Prediction
|
Study place:
|
Department of Informatics and Chemistry, FCT, VŠCHT Praha
|
| Guaranteeing Departments: |
Department of Informatics and Chemistry
|
| Supervisor: |
Ing. Martin Šícho, Ph.D.
|
| Expected Form of Study: |
Full-time |
| Expected Method of Funding: |
Scholarship |
| |
Annotation
Thanks to advanced machine learning techniques, protein structure prediction has recently become a fast-developing field of computational biology with tremendous potential, potentially enabling new insights into molecular functions and facilitating drug discovery. Recent advancements, such as AlphaFold, Boltz, Chai, ESMFold, OpenFold, or RoseTTAFold have revolutionized the field by achieving unprecedented accuracy in predicting protein 3D structures based solely on amino acid sequences. However, limitations like handling intrinsically disordered regions, effects of mutations and modeling ligand-bound states persist.
This PhD project aims to leverage protein structure prediction models to address critical biological questions, particularly in disease characterization and drug target identification. It will explore novel hypotheses for understanding disease mechanisms and identifying therapeutic targets. Additionally, the research will evaluate emerging protein structure elucidation techniques for virtual screening and binding affinity predictions of biologically relevant ligands, emphasizing their potential in structure-based drug design. The project will also focus on evaluating and improving current models by identifying and addressing limitations, such as predicting conformational flexibility or effects of mutations.
Studying the behavior of fish populations using biotelemetry data
|
Study place:
|
Department of Informatics and Chemistry, FCT, VŠCHT Praha
|
| Guaranteeing Departments: |
Department of Informatics and Chemistry
|
|
Also available in study programmes:
|
Drugs and Biomaterials (FCT) (
in English language
)
|
| Supervisor: |
Mgr. Jan Pačes, Ph.D.
|
| Expected Form of Study: |
Combined |
| Expected Method of Funding: |
Not funded |
| |
Annotation
Abstract: 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.
Computational mass spectrometry
|
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
|
|
Also available in study programmes:
|
Bioinformatics (
in English language
)
|
| Supervisor: |
Mgr. Tomáš Pluskal, Ph.D.
|
| Expected Form of Study: |
Full-time |
| Expected Method of Funding: |
Scholarship |
| |
Annotation
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.
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