Degree Programmes

Bioinformatics and Biocomputing

Full-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

  • 79 %

    students gain practical experience

  • 98 %

    students successfully pass the State Final Examination

  • 99 %

    graduates find work in the month

  • 40 938 Kč

    is the average starting salary for graduates

1st Year

V 1. semestru, kromě povinných teoretických předmětů z informatiky, získáte  přehled i v oblastech molekulární genetiky a strojového učení. Ve 2. semestru, pokud zvolíte doporučený průchod studiem, přijdou klíčové předměty specializace pokrývající bioinformatické algoritmy a principy biologií inspirované umělé inteligence. 

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.

They will pass on all their knowledge and hold you in difficult moments

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