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 Bratislava

Applications for study must be submitted through a portal of relevant faculty (FIIT STU, FEI STU until 31.5.2020, and FEI TUKE till 4.6.2020).

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: 31.5.2020th
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 2020/2021:

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
IT approaches for modeling of speech communication Beňuš Š. (annotation) Informatics
FIIT STU
Conversation coordination and mind-reading Beňuš Š. (annotation) Informatics
FIIT STU
Modelling and Control of Discrete-Event Systems by Means of Petri Nets Čapkovič F. (annotation) Informatics – FEI TUKE
Cybernetics – FEI STU
Parallelization of computer simulation of fires Glasa J. (annotation) Informatics
FMFI UK
FIIT STU
FEI TUKE
“Smart” models of automatic manufacturing systems based on artificial intelligence Havlík Š. (annotation) Informatics – FEI TUKE
Cybernetics – FEI STU
Application of artificial intelligence methods in the design of (smart) robotic devices Havlík Š. (annotation) Informatics – FEI TUKE
Cybernetics – FEI STU
Inteligent robotic systems based on smart elements and devices Havlík Š. (annotation) Cybernetics – FEI STU
Smart parts and devices Havlík Š. (annotation) Cybernetics – FEI STU
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
Deep neural networks for applications in image processing and computer vision Malík P. (annotation) Cybernetics
FEI STU
Deep neural networks for applications in image processing and computer vision Malík P. (annotation) Informatics
FIIT STU
Soft computing for complex solutions Nguyen G. (annotation) Informatics
FMFI UK
FIIT STU
FEI TUKE
Sentiment analysis in text 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 2020/2021
    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:

 

PhD students – 2017/2018

Applied informatics
PhD student: Supervisor:
Ing. Ondrej Kachman Doc. Ing. L. Hluchý, CSc., consultant: Ing. M. Baláž, PhD.
RNDr. Andrej Ridzik Doc. Ing. L. Hluchý, CSc., consultant: Ing. M. Baláž, PhD.
Cybernetics
PhD student: Supervisor:
Ing. Kasanický Tomáš Ing. I. Budinská,PhD.
Ing. Juraj Pristach Ing. I. Budinská, PhD.

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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IT approaches for modeling of speech communication
Supervisor: Prof. Mgr. Štefan Beňuš, PhD.
Department: Speech analysis and synthesis
Interpersonal speech communication is a complex system of relationships mainly at two levels: in the traditional approach between the physical characteristics of the speech signal and the multilayered set of speaker’s goals, intentions and knowledge, and in the innovative approach of recent years in how the physical and cognitive domains of the interlocutors influence each other and contribute to the success of the resulting speech interaction. The research will focus on the processing of speech dialogue databases, data mining at both mentioned levels, and implementation of acquired knowledge into communication between man and an automated system (avatar or robotic head).
Key words: speech signal, data mining, human-machine communication
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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Conversation coordination and mind-reading
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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Modelling and Control of Discrete-Event Systems by Means of Petri Nets
Supervisor: doc. Ing. František Čapkovič, PhD.
Department: Discrete processes modelling and control
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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Parallelization of computer simulation of fires
Supervisor: RNDr. Ján Glasa, CSc
Consultant: Ing. Lukáš Valášek, PhD.
Department: Parallel Computational Methods and Algorithms
Advances in computer systems and information technologies have allowed the development of program systems capable to model complex phenomena related to fire. Such systems achieve a significant level of reliability and credibility. However, there is a lack of knowledge and experience related to parallelization of modelling fires in large spaces which is real motivation for this research. The research is focused on ways of parallelization which accelerate the calculation, but their impact on reliability and accuracy of simulation has not been sufficiently resolved yet. New knowledge applicable to solving practical problems of fire safety are expected. Calculations will be realized on high performance computer cluster at the Institute of Informatics, Slovak Academy of Sciences, Bratislava.

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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.
Department: Sensor systems
Problems of optimal design of compliant electro-mechanical devices using artificial intelligence methods are expected. The work is based on the current design procedures with a focus on the design and optimization of the 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, movement units, etc., with a particular focus on small and microelectro-mechanical devices. The methodical procedure is to create mathematical models based on theoretical analyzes and experimental measurements (eg on physical models at a given scale) and subsequent optimization of parameters for real dimensions.

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Inteligent robotic systems based on smart elements and devices
Supervisor: Ing. Štefan Havlík, DrSc.
Department: Sensor systems
The topic concerns intelligent robotic systems and creating special systems from smart parts and devices as functional parts/structures for more complex tasks in robotics. Each such element/device performs, in addition to its functional properties, scanning tasks and processing information from several different sensors eventually with data and functioning fusion (diagnostics, learning – recognition, …) in order to integrate it into more complex robotic systems for a given purpose through communication; e.g. via Internet of Things (IoT). The aim is to design robotic systems with a high degree of reliability and safety (fault-tolerant systems) or to implement the concept of “zero defect strategy” for production technology cases.

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Smart parts and devices
Supervisor: Ing. Štefan Havlík, DrSc.
Department: Sensor systems
The topic concerns smart part and devices, as functional parts of intelligent robotic systems that enable the creation of purpose-built kits for more complex tasks in robotics. Each such element or device is intended to perform tasks (sensing, communication) in addition to functional properties, so that it can be automatically integrated into more complex robotic systems for a defined purpose.
The goal is to integrate processing of information from several different sensors with fusion to control the robotic system.
The aim (scientific contribution) is to design a highly reliable information system for technological applications. Design and development of an information system model that consists of several sensory systems.

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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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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. The topic will be studied at the Institute of Informatics of the Slovak Academy of Sciences.
Key words: deep learning, convolution neural networks, neural networks architecture, detection, instance segmentation, image processing, computer vision, neural network inference

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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
Significant advances of artificial neural networks abilities in recent years have allowed their wider use in increasingly complex tasks. In a number of specific tasks, they surpassed the skills of a highly qualified human being – the expert, and more accurately evaluated presented data. Superhuman abilities were demonstrated in the image data classification of common objects, recognition and classification of human faces at various observation angles, as well as in classification of highly specific medical data from X-rays. The area of computer image processing has shown the greatest progress in reducing the error rate by using the specially designed and trained deep neural networks. Every year, new international open challenges focusing on a specific area of computer image processing and computer vision are announced. There is also growing interest from industry, and many of these challenges are directly supported by the private sector. One important factor in the success of artificial neural networks is a significant increase in the depth of modern neural network models in conjunction with a reduced number of parameters based on the convolutional principle and optimized architecture. Neural network architecture still offers wide space for improvement. There is also a lot of room for improvement in more complex tasks such as detection and instant segmentation where human abilities have not yet been overcome. Research will focus on these areas with the aim to develop new methods, algorithms, architectures to improve the capabilities of neural networks and their effective application in practice.

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Soft computing for complex solutions
Supervisor: Ing. Giang Nguyen, PhD.
Department: Parallel and distributed information processing
The thesis topic is focused on the research in soft computing (SC), which constructs computationally intelligent methods by combining edge technologies such as artificial neural networks, machine learning, deep learning, as well as optimization, fuzzy logic, and probabilistics to solve complex problems. Various methods used in soft computing are neither independent of nor compete with one another, but rather, they work in a cooperative and complementary way. Soft computing aims to tolerance of imprecision, uncertainty, partial truth and approximation to achieve effectiveness and low solution cost. When today data has largescale potential with Volume, Velocity, Variety and Veracity characteristics, the wider collaboration between intelligent soft computing methods, scalable data preprocessing and highperformance backgrounds is practical to face challenges in many areas. All of these advanced technologies do not have to be always coupled together, but the alliance is essential for complex solutions.

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Sentiment analysis in text
Supervisor: Ing. Giang Nguyen, PhD.
Consultant: Ing. Štefan Dlugolinský, PhD.
Department: Parallel and distributed information processing
The thesis topic is focused on methods for sentiment analysis in text resources, which is a research subfield of natural language processing. It is an opinion mining, most often from product reviews, microblogs or from news articles in order to get marketing information, or overview of the opinion spectrum respectively in contexts. The thesis will continue from the state-of-the-art, it will design and validate appropriate methods applicable also to Slovak language. However, the topic is not limited to one specific language but may also explore methods suitable for other languages or among languages. The topic is closely related to recommender systems, which offer a variety of modern approaches to be taken such as natural language processing, deep learning as well as scalable data processing like Apache Spark.

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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. Although several standardizations and solutions in this area have emerged, they have not yet brought any comprehensive solution for the service development and deployment issue on IaaS clouds.
Therefore, from the view of general cloud users, they need to have an instrument, which can solve the problem. 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 important parts of international transport systems. Therefore, fire safety issues in tunnels must be considered seriously. Research will be focused on problems related to computer modelling of flows in road tunnel using the FDS program system which allows modelling and realistically visualizing flows caused by fire and simulating reaction of safety systems of the tunnel. Problems related to modelling of conditions of smoke stratification in tunnel tube during evacuation and self-rescue phases will be a part of investigation. Simulations will be realized on high performance computer cluster at the Institute of Informatics, Slovak Academy of Sciences, 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 reccomendations. New knowledge and methods applicable to practical problems, especially in the field of intelligent agriculture, are expected.

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