Postgraduate doctoral studies on Institute of informatics, Slovak academy of sciences

The Institute of Informatics Slovak Academy of Science is an external educational institution for doctoral study according to §54 of the law No.131/2002. The Institute realizes the PhD study on the basis of agreements on cooperation in the implementation of doctoral with universities for the following study programs:
Applied Informatics in the field of study Informatics with the Faculty of Informatics and Information Technologies STU in Bratislava
Informatics in the field of Informatics with the Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava and the Faculty of Electrical Engineering and Informatics TU in Košice
Robotics and Cybernetics as part of the Cybernetics study program with the Faculty of Electrical Engineering and Informatics STU in BratislavaApplications for study must be submitted through a portal of relevant faculty (FIIT STU, FEI STU until May 31 2022, and FEI TUKE till June 2 2022).For doctoral study in frame of agreemant with FMFI UK send filled out application form https://www.portalvs.sk/files/cep/prihlaska3-stupen.pdf together with a short cv, a motivation letter for a chosen topic, a list of publications, a copy of a document that prove the highest degree achieved and a list of subjects completed during university study to the Institute of Informatics SAS, Dúbravská cesta 9, 845 07 Bratislava. (Electronic and personal delivery of applications will not be accepted.)
Application deadline: May 31 2022
The admission procedure will take place in June. The date will be anounced. There is no fee for the admission procedure.

PhD thesis for academic year 2022/2023:

Theme: Supervisor: Annotation: Program with Faculty:
New Methods for Large-scale Real-time Collection, Aggregation and Processing of Geo-mapped Data Balogh Z. (annotation) Informatics
FIIT STU
FEI TUKE
The influence of alignment
Beňuš Š. (annotation) Informatics
FIIT STU
Well-being and safety in inteligent human-cyber-physical systems Budinská I. (annotation) Informatics – FEI TUKE
Cybernetics – FEI STU
Modelling and Control of Discrete-Event Systems by Means of Petri Nets Čapkovič F. (annotation) Informatics – FEI TUKE
Cybernetics – FEI STU
Computer modelling of road tunnel fire Glasa J. (annotation) Informatics
FMFI UK
FIIT STU
FEI TUKE
Computer modelling of vegetation fire Glasa J. (annotation) Informatics
FMFI UK
"Smart" models of automatic manufacturing systems based on artificial intelligence Havlík Š. (annotation) Cybernetics – FEI TUKE
Application of artificial intelligence methods in the design of (smart) robotic devices Havlík Š. (annotation) Informatics – FEI TUKE
Distributed large data processing Hluchý L. (annotation) Informatics
FMFI UK
FIIT STU
FEI TUKE
Artificial Intelligence Methods in Cyber Security Hluchý L. (annotation) Informatics
FMFI UK
FIIT STU
FEI TUKE
"Smart" models of automatic manufacturing systems based on artificial intelligence Hričko J. (annotation) Cybernetics – FEI STU
Application of artificial intelligence methods in the design of (smart) robotic devices Hričko J. (annotation) Cybernetics – FEI STU
Deep neural networks for applications in image processing and computer vision Malík P. (annotation) Informatics
FMFI UK
FIIT STU
FEI TUKE
Cybernetics
FEI STU
Soft computing for complex solutions Nguyen G. (annotation) Informatics
FMFI UK
FIIT STU
FEI TUKE
Machine learning and sensitive data protection Nguyen G. (annotation) Informatics
FMFI UK
FIIT STU
FEI TUKE
Automatic detection of Alzheimer’s disease by patient speech analysis Rusko M. (annotation) Informatics
FMFI UK
Automatic detection of Parkinson’s disease by patient speech analysis Rusko M. (annotation) Informatics
FEI TUKE
Automatic detection of symptoms of the neurodegenerative diseases of the brain by patient speech analysis Rusko M. (annotation) Informatics
FIIT STU
Automatic measurement of stress in human voice Rusko M. (annotation) Informatics
FMFI UK
FIIT STU
FEI TUKE
High-end expressive speech synthesis in Slovak Rusko M. (annotation) Informatics
FMFI UK
FIIT STU
FEI TUKE
Automatic multi-modal adaptation of personal robotic assistant to user’s personality Rusko M. (annotation) Cybernetics
FEI STU
Emotions in communication with robotic system Rusko M. (annotation) Cybernetics
FEI STU
Computer games for automatic collection of expressive speech data Rusko M. (annotation) Cybernetics
FEI STU
New methods for development, deployment and orchestration of cloud services Dinh Viet Tran (annotation) Informatics
FMFI UK
FIIT STU
FEI TUKE
Computer modelling of flows in road tunnel during fire Weisenpacher P. (annotation) Informatics
FMFI UK
FIIT STU
FEI TUKE
Intelligent methods for multispectral data analysis Zelenka J. (annotation) Cybernetics
FEI STU
Informatics – FEI TUKE

Studying at the Institute of Informatics SAS has the following benefits:

  • Working with up-to-date technologies and access to a powerful computing cluster of the II SAS.
  • Involvement in research projects at national and international levels.
  • Cooperation in production of interesting applications for practice.
  • International mobility, financial coverage of travel expenses for active participations in international conferences.
  • Ph.D. internship at foreign scientific institutions.
  • The possibility of accommodation in a boarding house of SAS (in Devinska Nova Ves, 15 min. by bus from II SAS) in renovated apartments for a good price (75 € / month).
  • A scholarship for the academic year 2021/2022
    a) scholarship at least € 807,50 (before completing a state degree exam) + possibility of a surcharge when working on projects,
    b) scholarship at least € 940,50 (after completing a state degree exam) + possibility of a surcharge when working on projects.
  • Bonus for a successful Dissertation thesis defense.

Other employee benefits

  • flexible work schedule, weekly working time of 37,5 hours
  • additional 5 paid vacation days
  • advantageous reimbursement of sick leave
  • bonuses for publications and year-end bonuses
  • bonuses or incentives for publications (according evaluation by the Scientific board of II SAS)
  • Recreational options – holiday stay in the Congress centre in Smolenice, the Academia Congress centre in Stará Lesná, and an institutional owned cottage in Vyhne
  • flexible working time
  • teambuilding activities (an institutional field day e.g. in Smolenice castle)

If you have questions or would you like to work with us, contact us:

 

Annotations:

New Methods for Large-scale Real-time Collection, Aggregation and Processing of Geo-mapped Data
Supervisor: Ing. Zoltán Balogh, PhD.
Department: Parallel and distributed information processing
The research is focusing on the topic of large-scale collection of geographically mapped structured or semi-structured data from a large number of sensors. Collection of data occurs from mobile computing devices such as smart mobile phones, smart watches or single-board computers. Research can also tackle topics such as contextual real-time notification, semantic data modelling, continuous visualization of dynamically aggregated information or a design of a novel distributed architecture for credible and secure real-time data collection using specialized data aggregation points in the network. There is an opportunity to exploit the research results in projects in the domains of crisis management support or intelligent transportation control.

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The influence of alignment
Supervisor: Prof. Mgr. Štefan Beňuš, PhD.
Department: Speech analysis and synthesis
Recent research shows that entrainment (alignment, accommodation) between people during speech interaction can have a positive impact on the success of communication, perception of an interlocutor, or promote mutual trust. This research involves implementing speech entrainment functionality into human-machine communication, and using speech signal processing and machine learning methods for analyzing how speech entrainment affects user’s behavior, decision-making, or emotional state when the user communicates with another person or an automated system (avatar or robotic head).
Key words: speech entrainment, human-machine communication, automatic dialogue system, speech signal processing, machine learning
Positions are within EU Marie Curie ITN grant (https://www.cobra-network.eu/) and are available for EU and non-EU applicants who have not resided or carried out their main activity (work, studies) in Slovakia for more than 12 months in the 3 years immediately before their appointment.

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Well-being and safety in inteligent human-cyber-physical systems
Supervisor: Ing. Ivana Budinská, PhD.
Department: Modelling and Control of Discrete Processes
The topic is focused on a study of potential vulnerabilities in cyber-physical systems (CPS). CPS are complex systems in which the physical (mechatronic, mechanical) components of the system are monitored and controlled through information and communication technologies (ICT). Such systems can be e.g. smart-grid, autonomous vehicles and groups of autonomous vehicles, telemedicine systems, intelligent buildings, etc. After gaining access to the system, an attacker can monitor the system and then detect vulnerabilities and take control of certain parts or the entire system. The thesis will focus on the detection of anomalies in the behavior of a selected type of CPS using machine learning and data mining algorithms.

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Modelling and Control of Discrete-Event Systems by Means of Petri Nets
Supervisor: doc. Ing. František Čapkovič, PhD.
Department: Modelling and Control of Discrete Processes
Discrete-event systems (DES) are discrete in nature. A DES system remains in its current state until it is forced to change it due to the occurrence of a discrete event. Many types of systems in social practice have a character of DES – e.g. Flexible Manufacturing Systems (FMS), robotized cells, discrete production lines, some types of transport systems, communication systems, etc. Petri nets (PN) of different species are an appropriate tool for the mathematical modelling of DES. They also make it possible to synthesize the control of DES – i.e. the synthesis of supervisors. The purpose of this assignment is to design the method for the supervisor synthesis by analysing the structure of the PN model by means of siphons and traps in order to eliminate deadlocks.

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Computer modelling of road tunnel fire
Supervisor: RNDr. Ján Glasa, CSc
Consultant: Ing. Lukáš Valášek, PhD.
Department: Parallel Computational Methods and Algorithms
Computer modelling of phenomena related to fire has achieved high degree of reliability and credibility. Existing fire simulators are capable to incorporate wide variety of physical processes into calculation and model the course of fire and its effects in large environments. Research will focus on modelling and visualization of fire in road tunnel using the FDS program system. Experimental data gathered during tests in a real motorway tunnel will be used for simulation validation. The automatic response of the tunnel control system to an emergency situation, the aim of which is to create and maintain conditions for safe evacuation of people, will also be the subject of the research. Simulations will be realized on high-performance computing infrastructure in Slovak Academy of Sciences.
Keywords: computer modelling of fires, road tunnel, FDS, fire course, parallel calculation, HPC

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Computer modelling of vegetation fire
Supervisor: RNDr. Ján Glasa, CSc
Consultant: Ing. Lukáš Valášek, PhD.
Department: Parallel Computational Methods and Algorithms
Computer modeling of vegetation fire is a complex interdisciplinary problem. Several program systems capable to model the spread of vegetation fire including a wide variety of physical processes related to fires into the calculation have been developed. The aim of the research is to understand the principles and models used for simulation of vegetation fire in the FDS program system and to test and verify selected parts of the system by modelling of a real vegetation fire. The research will also include the processing of experimental data obtained during fire tests and their use to verify the results of computer simulation. New results and experience in the field of effective realization of computer simulation on HPC infrastructure in Slovak Academy of Science are also expected.
Keywords: computer modelling, vegetation fire, FDS, fire propagation, parallel calculation, HPC

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"Smart" models of automatic manufacturing systems based on artificial intelligence
Supervisor: Ing. Štefan Havlík, DrSc.
Department: Sensor systems
The topic is focused on research and development of new artificial intelligence based methods and algorithms for processing of data of sensors in technological processes. The goal is to create models of object states and to obtain behavioral characteristics for optimal problem solving and design of functional parameter changes. Each production system consists of a sequence of technological operations, with the operation of the product being sensed by sensors, and the information is transferred together with the product. The final "quality" of the product is evaluated after a certain number of operations or after the production process has been completed.The aim (scientific contribution) is to create a method that enables diagnosis,
decision-making and correction when a deteriorated product quality is detected.
Note: The topic is related to Industry 4.0

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Application of artificial intelligence methods in the design of (smart) robotic devices
Supervisor: Ing. Štefan Havlík, DrSc.
Consultant: J. Hricko
Department: Sensor systems
The area of interest concerns the solution of intelligent robotic systems and the creation of special configurations from the “SMART” elements and devices as functional parts (structures) for more complex tasks in robotics. Each element/device, in addition of its functional requirements, solves tasks of sensing and processing information from several (different) sensors with potential data fusion (diagnostics, learning – recognition, …) in order to be integrated into more complex robotic structures/assemblies oriented to a given purpose through communication; e.g. Internet of Things (I.o.T). The aim is to solve robotic systems with a high degree of reliability/safety (fault-tolerant systems) or to implement the concept “zero defect strategy” for the production technologies cases, as well.
New solutions of design problems of optimal robotic / MEMS devices using artificial intelligence methods are expected. The elaboration could go out from current design techniques focusing to design and optimization of geometry, parameters and functional characteristics of E-M devices. Implementation of “smart” properties into the solved structure of electro-mechanisms, such as e.g. sensors, effectors, actuators, etc., with a specific focus on small and micro-electro-mechanical devices (MEMS) is expected. The methodical procedure is to create mathematical models based on theoretical analyzes and experimental measurements (on physical models at a given scale) and subsequent optimization of parameters for real dimensions.

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Distributed large data processing
Supervisor: Doc. Ing. L. Hluchý, CSc.
Department: Parallel and distributed information processing
Rapidly increasing volumes of diverse data from distributed sources create challenges for extracting valuable knowledge. Such applications can be considered modelling, simulation, pattern recognition, visualization, etc. in different areas as e.g. biomedicine, astrophysics, environmental sciences, aeronautics, automotive, energy, material sciences. Due to the size of the data, which are often referred to as large, extreme, it is necessary to design a methodology, robust methods and tools for extreme-scale analytics in synergy with distributed architectures for collecting and managing vast amounts of data such as Cloud Technologies and IoT. The dissertation project will be focused on the analysis, design of methodology, methods and algorithms for processing of large data for selected applications, which are currently solved at UI SAV. The research project will also include research and development of appropriate tools and services for distributed processing of methods and algorithms.

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Artificial Intelligence Methods in Cyber Security
Supervisor: Doc. Ing. L. Hluchý, CSc.
Department: Parallel and distributed information processing
Most current approaches to computer security focus on specific aspects of information and communication technology systems such as access control, cryptography, anonymization, virus protection, antivirus detection, intrusion detection, and anomaly detection. However, they lack an overall view of many aspects of cyber threats and do not pay due attention to one of the most important elements of cyber security: the human aspect. In addition, they often fail to address the dynamic nature of cyber attacks that are rapidly evolving and become more sophisticated by using new vulnerabilities and combining various attack channels (network, physical, human, etc.). To address these constraints and to increase our detection and response capabilities, we need a systematic and holistic approach to cyber security that takes into account technological and human factors. The dissertation project will focus on the design of methodology and methods for the analysis of anomalies and abnormalities using techniques of data acquisition and machine learning (data mining and processes mining) with the possibility of detection of hitherto unknown threats and vulnerabilities.

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"Smart" models of automatic manufacturing systems based on artificial intelligence
Supervisor: Ing. Jaroslav Hričko, PhD.
Department: Sensor systems
The topic is focused on research and development of new artificial intelligence based methods and algorithms for processing of data of sensors in technological processes. The goal is to create models of object states and to obtain behavioral characteristics for optimal problem solving and design of functional parameter changes. Each production system consists of a sequence of technological operations, with the operation of the product being sensed by sensors, and the information is transferred together with the product. The final "quality" of the product is evaluated after a certain number of operations or after the production process has been completed.The aim (scientific contribution) is to create a method that enables diagnosis,
decision-making and correction when a deteriorated product quality is detected.
Note: The topic is related to Industry 4.0

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Application of artificial intelligence methods in the design of (smart) robotic devices
Supervisor: Ing. Jaroslav Hričko, PhD.
Consultant: J. Hricko
Department: Sensor systems
The area of interest concerns the solution of intelligent robotic systems and the creation of special configurations from the “SMART” elements and devices as functional parts (structures) for more complex tasks in robotics. Each element/device, in addition of its functional requirements, solves tasks of sensing and processing information from several (different) sensors with potential data fusion (diagnostics, learning – recognition, …) in order to be integrated into more complex robotic structures/assemblies oriented to a given purpose through communication; e.g. Internet of Things (I.o.T). The aim is to solve robotic systems with a high degree of reliability/safety (fault-tolerant systems) or to implement the concept “zero defect strategy” for the production technologies cases, as well.
New solutions of design problems of optimal robotic / MEMS devices using artificial intelligence methods are expected. The elaboration could go out from current design techniques focusing to design and optimization of geometry, parameters and functional characteristics of E-M devices. Implementation of “smart” properties into the solved structure of electro-mechanisms, such as e.g. sensors, effectors, actuators, etc., with a specific focus on small and micro-electro-mechanical devices (MEMS) is expected. The methodical procedure is to create mathematical models based on theoretical analyzes and experimental measurements (on physical models at a given scale) and subsequent optimization of parameters for real dimensions.

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Deep neural networks for applications in image processing and computer vision
Supervisor: Ing. Peter Malík, PhD.
Department: Design and diagnostics of digital systems
Artificial neural networks are becoming very widely used in practical applications. They are used to process a large amount of information contained in image, audio or text data. Their practical applications have varying levels of complexity, from the search of the most important data, complete data analysis, prediction and forecasting to the calculation of control signals in the form of an autonomous control system. Image processing is a specific area in which artificial neural networks achieve excellent results due to their ability to learn to recognize the most important features and characteristics of an image from a large number of image pixels. In simpler computer vision tasks, such as object classification, artificial neural networks perform better than humans. This has been proven in a number of application domains, including general object recognition, biometric data classification (face, gait), medical data recognition (X-ray, CT, MRI).
The current challenge for artificial neural network research is the more complex tasks of computer vision such as detection and instance segmentation in which human capabilities have not been surpassed. Equally important research task is to reduce hardware requirements of artificial neural network computation. Research into new efficient artificial neural network architectures has made a significant contribution in this area. It is the architecture of neural networks that still offers a wide scope for improvement as solutions are sought at a higher level of problem abstraction. Research thesis will focus on these research areas in order to develop new methods, algorithms or architectures that improve the parameters and capabilities of artificial neural networks and enable them to be effectively applied in practice. Priority research areas will be adapted after consultation and student participation in international competitions and active participation in international conferences are foreseen.
Key words: deep learning, convolution neural networks, neural networks architecture, detection, instance segmentation, image processing, computer vision, neural network inference

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Soft computing for complex solutions
Supervisor: Ing. Giang Nguyen, PhD.
Department: Parallel and distributed information processing
The topic is focused on soft computing, which constructs computationally intelligent methods by combining edge technologies such as machine learning, neural networks, and deep learning to solve complex domain problems like network monitoring, resource management. Various methods used in soft computing are neither independent of nor compete, but rather, they work in a cooperative and complementary way. Soft computing aims for tolerance of imprecision, uncertainty, partial truth, and approximation to achieve effectiveness and low solution cost. When today data has large-scale potential with many V’s characteristics, the wider collaboration between smart data-centric AI methods, scalable data processing, and high-performance support is practical to face challenges in many areas. All of these advanced technologies do not have to always merge together, but the alliance is essential.

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Machine learning and sensitive data protection
Supervisor: Ing. Giang Nguyen, PhD.
Department: Parallel and distributed information processing
Many modern-world problems, which we want to solve with the help of AI, require direct access to sensitive data such as personal, medical, business, or security information. In many cases, getting access to such information is nearly impossible due to the protection requirements. From this point, several questions are raised, such as how to avoid sensitive information disclosure in the data science process or moreover, how can we model sensitive data without direct access to them. The topic of the work is focused on collaborative (federated) learning, where it is necessary to ensure knowledge sharing between partners, while ensuring the protection of sensitive data at the same time.

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Automatic detection of Alzheimer’s disease by patient speech analysis
Supervisor: Ing. Milan Rusko, PhD.
Department: Speech analysis and synthesis
The object of this work is to design and implement a system for automatic detection of symptoms of Alzheimer’s disease via automatic analysis of patient’s speech. The student will review the state-of-the-art in this non-invasive screening and diagnostic method in the world. He will analyze the approaches that are using acoustic and linguistic characteristics as well as machine learning techniques. In the practical part he will design, implement and evaluate a program for automatic detection of Alzheimer’s disease by analyzing the patient’s speech.

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Automatic detection of Parkinson’s disease by patient speech analysis
Supervisor: Ing. Milan Rusko, PhD.
Department: Speech analysis and synthesis
The object of this work is to design and implement a system for automatic detection of symptoms of Parkinson’s disease via automatic analysis of patient speech.
The student will review the state-of-the-art in this non-invasive screening and diagnostic method in the world. He will analyze the approaches that are using acoustic and linguistic characteristics as well as machine learning techniques. In the practical part he will design, implement and evaluate a program for automatic detection of Parkinson’s disease by analyzing the patient’s speech.

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Automatic detection of symptoms of the neurodegenerative diseases of the brain by patient speech analysis
Supervisor: Ing. Milan Rusko, PhD.
Department: Speech analysis and synthesis
The object of this work is to design and implement a system for automatic detection of symptoms of the neurodegenerative diseases of the brain via automatic analysis of patient speech. The student will review the state-of-the-art in this non-invasive screening and diagnostic method in the world. He will analyze the approaches that are using acoustic and linguistic characteristics as well as machine learning techniques. In the practical part he will design, implement and evaluate a program for automatic detection of the symptoms of the neurodegenerative disease of the brain by analyzing the patient’s speech.

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Automatic measurement of stress in human voice
Supervisor: Ing. Milan Rusko, PhD.
Department: Speech analysis and synthesis
The aim of the work is to to prove the concept of identifying the stress level speaker by analyzing his speech. The doctoral student will give an overview of the state-of-the-art solutions.
He will analyze the most commonly used methods that use acoustic and linguistic cues as well as machine learning techniques. He will design, implement and evaluate a system for automatic detection of actual emotions from speech.

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High-end expressive speech synthesis in Slovak
Supervisor: Ing. Milan Rusko, PhD.
Department: Speech analysis and synthesis
The aim of the thesis is to record a speech database and create a speech synthesizer in Slovak using the latest machine learning technologies, which will be able to generate a voice with higher levels of emotional activation – arousal (excited, urgent, warning) as well as a voice with lower level of arousal (calm, soothing). The student will also try to create a voice expressing negative emotions and a voice expressing positive emotions. The voice will be implemented in the voice assistant.

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Automatic multi-modal adaptation of personal robotic assistant to user’s personality
Supervisor: Ing. Milan Rusko, PhD.
Department: Speech analysis and synthesis
The subject of this work is to design and implement a system for automatic classification of the personality type of the user from his speech and the way he controls the system.
The student will analyze the current state of automatic recognition of personality in the world. He will review the methods using acoustic and linguistic cues from speech, as well as analysis of other modes of user‘s control of the system. He will design, implement and evaluate a program for automatic detection of the user’s personality and design and implement the adaptation of the behavior and speech of the robotic personal assistant to the detected user’s personality type.

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Emotions in communication with robotic system
Supervisor: Ing. Milan Rusko, PhD.
Department: Speech analysis and synthesis
The aim of the thesis is to verify the possibility of implementing an automatic response to user’s emotional behavior in the user interface of the home robotic system (assistant).
The system evaluates the user’s actual emotions by analyzing his or her speech and subsequently adjusts the behavior and speech performance of the home robotic system appropriately.
The doctoral student will prepare an overview of the current state of the solutions in the world. He will analyze the most commonly used methods of measuring emotion from speech using acoustic and linguistic cues as well as machine learning techniques. He will design and implement a system for automatic detection of basic emotions from speech.
He will propose a robotic system’s response to emotions and integrate it into the user interface.

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Computer games for automatic collection of expressive speech data
Supervisor: Ing. Milan Rusko, PhD.
Department: Speech analysis and synthesis
Research and development in the field of artificial intelligence requires a deep knowledge of human communication. Speech databases are one source of such knowledge.
The aim of this work is to design a voice-controlled computer game collecting recordings of players’ speech in order to create a database of expressive speech and speech under stress.
The doctoral student will analyze the state of the art in automatic emotions recognition from speech. He will give an overview on using games to collect speech data. He will design and implement a voice-controlled game with continuous voice recording, which will evoke stress and emotion in the players. The recorded speech will be used to create an annotated database of expressive speech.

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New methods for development, deployment and orchestration of cloud services
Supervisor: Ing. Dinh Viet Tran, PhD.
Department: Parallel and distributed information processing
With the advance of cloud technologies, the trend of developing and deploying services/applications in cloud environment also has appeared. There are economic as well as technological reasons why an application should be developed and deployed on the cloud. On the economic side, cloud computing can provide significant cost savings due to the increased utilization resulting from the pooling of resources (often virtualized). Furthermore, cloud computing enables rapid delivery of IT services, which increases business efficiency. This is the reason, Cloud Computing attracts large enterprises/companies to build and provide outside Cloud services in order to make a profit. However, from the comprehensive view of the economy, the competition between providers always leads them to keep its own proprietary technologies and that tends to lock customers into their services. Therefore, from the view of general cloud users, they need to have a new approach and correspondent instrument, which can help users to deploy their services on any Cloud infrastructure. The basic of instrument is container technologies, suitable method for service description and workflow that automates whole lifecycle of the service from development to deployment. The aim of the thesis is to propose an approach, method and tools for solving the problem of service development, deployment and orchestration among different cloud infrastructures.

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Computer modelling of flows in road tunnel during fire
Supervisor: Mgr. Peter Weisenpacher, PhD.
Department: Parallel Computational Methods and Algorithms
Road tunnels are an important part of international transport systems; therefore, increased attention is paid to the fire safety of tunnels. The research will focus on problems related to computer modelling of flows in a highway tunnel using the FDS software system, which allows realistic modelling and visualization of flows generated by fire and simulates the operation of tunnel safety systems. The research will also include issues related to the parallel implementation of the simulation. The calculations will be performed on an efficient computing infrastructure at the Institute of Informatics of the Slovak Academy of Sciences in Bratislava.

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Intelligent methods for multispectral data analysis
Supervisor: Ing. Ján Zelenka, PhD.
Department: Discrete processes modelling and control
The topic is focussed on acquiring and processing of multispectral data from various sources (satellite, aerial images, in situ measurements). Thanks to the use of sensors in large geographic areas during long time periods, we obtain a huge amount of data eg. on agricultural crops, forests and so on. By combining this information with historical data and tacit knowledge of domain experts, it is possible to make decisions with the aim to increase yields, protect crops, reduce the use of chemicals and thus contribute to environmental sustainability. Research will also include management of information and real-time generation of recommendations. New knowledge and methods applicable to practical problems, especially in the field of intelligent agriculture, are expected.

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