Bioinformatics and Biocomputing
Acad. year 2025/2026Full-Time 2 Years Title Awarded Ing.
Bioinformatics and biocomputing is a specialization at the intersection of computer science and biology. You will learn to understand relevant principles of biology, e.g. from molecular genetics, evolutionary theory or neuroscience. However, computer science is fundamental - through it, you will learn to use, create and effectively implement algorithms for processing, analyzing and presenting biological data. This specialization assumes a deeper interest in biology and genetics and a high school-level knowledge of these fields. When you graduate, you will be a specialist in bioinformatics and biology-inspired artificial intelligence. You will be employed by biotechnology labs and companies working on intelligent data processing and AI applications.
Information technology moves the world
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79 %
students gain practical experience
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98 %
students successfully pass the State Final Examination
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99 %
graduates find work in the month
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40 938 Kč
is the average starting salary for graduates
1st Year
Compulsory Programme Courses - Winter
Compulsory Programme Courses - Summer
Compulsory Specialization Courses - Winter
Compulsory Specialization Courses - Summer
The common basis of the programme
The common core of the program consists of courses that will give you the knowledge important for all IT engineers:
- Computation Systems Architectures will teach you how to think about how your code will run on modern computing platforms, how to think about programming in a way that makes the most efficient use of resources, i.e., that your application makes the best use of the power of modern platforms, makes efficient use of system memory resources, and is also efficient in terms of energy consumed.
- Functional and Logic Programming will teach you that although classical imperative programming is a very widely used paradigm and is very close to machine-level implementation, there are other approaches that will give you a new perspective on some key problems and help you get novel and often more efficient solutions to them.
- Modern Trends in Informatics (in English) you need to know to see where the field is going and what to expect in practice in a few years.
- Parallel and Distributed Algorithms is a course that will show you the patterns, limits, and pitfalls of parallel and distributed algorithmic solutions and the associated synchronization mechanisms, without which you will hardly succeed in solving many of the more complex problems.
- Statistics and probability is the right hand of every engineer to process numerical results of experiments or data obtained while running your application, analyze them and learn from them to make further decisions is almost his daily bread.
- Theoretical Computer Science shows the limits of computer science capabilities through formal languages and mathematical models of computation. This is the only way to understand whether your problem is even solvable and, if so, with what resources and means to prove it.
- Data Storage and Preparation, especially big data, and extracting knowledge from it is a valuable art to any computer scientist. It is a key aspect that strongly influences the effectiveness of many solutions and applications.
- Artificial Intelligence and Machine Learning is a course where you will learn how to teach computers to understand our world and make them solve problems that are easy for humans but difficult for an algorithmic machine to handle.
Recommended course compositions
Recommended course composition NBIO1
1st year of study, winter semester
- Artificial Intelligence and Machine Learning
- Computation Systems Architectures
- Data Storage and Preparation
- Molecular Genetics I
- Statistics and Probability
- Theoretical Computer Science
1st year of study, summer semester
- Bio-Inspired Computers
- Bioinformatics
- Convolutional Neural Networks
- Functional and Logic Programming
- Parallel and Distributed Algorithms
- Machine Learning and Recognition
2nd year of study, winter semester
- Advanced Bioinformatics
- Knowledge Discovery in Databases
- Semester Project
- Brain Computer Interface
- Game Theory
- Graph Algorithms
2nd year of study, summer semester
They will pass on all their knowledge and hold you in difficult moments
Prof. Ing.
Sekanina Lukáš
Ph.D.
He lectures on Computer Systems Design (how it works and how to build a processor) and a master's course Bio-Inspired Computers. It deals with the use of biology-inspired artificial intelligence for the design and optimization of software and hardware. He leads the Evolvable hardware research group at FIT.
Doc. Ing.
Martínek Tomáš
Ph.D.
He teaches the Master's course Bioinformatics, which introduces basic methods and algorithms for analysing biological data, usually in the form of DNA molecules and proteins. It focuses on the development of new tools in genomics and proteomics.
Doc. Ing.
Zbořil František V.
CSc.
He has been dealing with artificial intelligence for more than 30 years and he is a pioneer of this discipline at FIT. Currently he teaches the Fundamentals of Artificial Intelligence, Soft Computing and Intelligent Systems courses. He is the guarantor of the field/specialization Intelligent Systems and he leads the research group of the same name.
What are we talking about?
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At the Faculty of Information Technology, we are aware that it is impossible to prepare our future graduates for their diverse career paths without contact with industry, which is why we have been building a portfolio of partners for a long time so that the range of focus is broad and at a high professional level. …
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FIT team helps build a unique dialect map
A team from the Faculty of Information Technology at the BUT, led by Martin Karafiát, is involved in a unique dialect mapping project. In cooperation with the Academy of Sciences of the Czech Republic and Palacký University in Olomouc, they are creating a website where you can select a region of the Czech Republic and listen to the dialects characteristic of a given place. …
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Other Master
Specializations
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Application Development
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Bioinformatics and Biocomputing
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Computer Graphics and Interaction
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Computer Networks
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Computer Vision
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Cyberphysical Systems
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Cybersecurity
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Embedded Systems
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High Performance Computing
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Information Systems and Databases
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Intelligent Devices
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Intelligent Systems
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Machine Learning
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Mathematical Methods
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