Free University of Bozen · Bolzano - PhD Programme computer science
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PhD Programme in Computer Science   Director of the Programme: Prof. Diego Calvanese

The PhD Programme lasts three years and the official language of the programme is English.

PhD students are expected to work full-time on their research. During the PhD, it is advisable to spend a period of 6-12 months at a National or International research center.

Candidates are strongly advised to contact their desired research centers at the Faculty of Computer Science before applying. This way, they can obtain a clear idea of the specific research carried out at the Faculty.

The Selection Committee selects PhD students based on a comparative assessment of the qualification of applicants, taking into account also feedback from potential supervisors, especially those who have grants available to support the PhD students, and determines the winners based on the merits of each candidate. The Selection Committee also selects those candidates who are qualified to start a PhD program at the Free University of Bozen/Bolzano, but who cannot be admitted for lack of space. Should a selected winner not accept the position, such candidates will be next in line for the position.

It is expected that students are able to write and speak fluently in English.




Course Objectives  

The PhD study programme places an emphasis on research. A primary goal of the course is to impart to candidates the capacity to solve complex problems by independently applying technical and scientific methods. This is achieved by immersing the candidates in activities that present specific demands such as:
  • to understand complex scientific work on the current state of the art in computer science;
  • to contribute to the work of the research group, which is equally characterised by innovative ideas as well as thorough expertise;
  • to develop his or her own approaches to research and to elaborate them as far as possible in order to achieve new results;
  • to work in an international field and to exchange and to disseminate scientific findings.

Upon successful completion of the degree of doctor of computer science, the graduate should exhibit the following skills and expertise:
  • Substantial problem-solving abilities: Graduates take on complex problems with an innovative approach, with which they are familiar through their understanding of the technical scientific literature, and develop new instruments for problem-solving independently.
  • The capacity for the communication and dissemination of results in the scientific field: The dissemination of scientific results is a substantial component of every activity in scientific sectors and the IT-industry. Graduates must therefore acquire skills in both written and verbal presentation of technical scientific topics: they must be capable of communicating their findings in writing, through scientific essays and papers, as well as verbally, in lectures.
  • Organisational Abilities: The activity of a doctor in computer science consists in large part of studies and investigations. During his or her studies, the graduate student learns, without precise working organizational specifications. Graduates must be able to organize their activities both individually and within their research groups, as well as to coordinate study and work. This last point refers to the individual student, his or her doctoral work, as well as students and other co-workers on research projects.
  • A thorough and comprehensive knowledge within the field of computer science, covering theoretical and formal aspects such as technology and methodologies.


Research Areas  

Research in the Faculty of Computer Science is focused on three areas that are treated on a long-term basis by research groups whose members collectively examine topics related to each of the three research areas. The research areas, with a selection of topics, are: 

Information and database systems engineering 
  • Spatial and temporal databases 
  • Approximation Techniques in databases
  • Query optimization in databases
  • Cooperatve interfaces for information access and filtering
  • Data mining techniques for preference elicitation and recommendation
  • Cloud computing and big data
  • Agile development & human aspects of software engineering
  • Software startups and open science
  • Design based Hardware engineering
  • Technology enhanced learning

The research activities in the area of database and information systems focus on key aspects of applied computer science, including data warehousing and data mining, the integration of heterogeneous and distributed databases, time-varying information, data models, and query processing. The research approach is primarily constructive in its outset, and it includes substantial experimental and analytical elements. The development activities cover the design of data models and structures, and the development of algorithms, data structures, languages, and systems. The experimental activities verify real world artifacts with the help of prototypes and simulations. The analytic activities include the analysis of the algorithmic complexity and the evaluation of languages. The main goal is theoretically sound results that solve real world problems.

Knowledge Representation and Databases

  • Logic based languages for knowledge representation
  • Intelligent access to databeses
  • Semantic technologies
  • Visual and verbal paradigms for information explporation  
  • Temporal aspects of data and knowledge
  • Extending database technologies
  • Inter-operation, verification, and composition of business services and processes
The research topics in knowledge representation are focused on foundational and practical aspects of knowledge representation technologies applied to information systems. The whole life cycle ranging from the design to the deployment of such technologies is covered: the conceptual modeling of various types of knowledge, the linguistic and logical aspects of knowledge, the integration of heterogeneous knowledge sources, including information coming from the Internet, the usage of knowledge to support the intelligent retrieval of information, and the usage of knowledge to create virtual services on the net.

Software Engineering
  • Agile methods, lean management, and open source
  • Measurement and study of software quality, reliability, evolution and reuse
  • Distributed computing and service-oriented architectures (mobile and distributes services)
  • IT and business alignment
  • Software reuse and component based development
  • Interoperability in collaborative systems
  • IT for automation
  • Energy-aware systems

The research topics in software engineering are focused on the empirical and quantitative study of innovative models for software development. The target analysis techniques include both traditional statistics, and new approaches, such as computational intelligence, Bayesian models, and meta-analytical systems. The innovative software development techniques include (a) methods based on lean management, such as agile methods, with a specific interest for benchmarking and identification of defects, and (b) open source development models, with specific attention for self organizing systems and the analysis of the resulting qualit

The doctoral works treat topics from the three research areas that develop from the ongoing work of the groups.

The following list contains possible topics for doctoral work: 

  • Optimizing a sequence of recommendations
  • Decision making, rationality and recommendations 
  • Fast real-time analysis of time series data
  • Scalable query processing in temporal databases 
  • Spatio-temporal query processing with MapReduce 
  • Robust query optimization for database management systems
  • Itinerary planning for tourist applications
  • Extreme apprenticeship and new methodologies in computer science education
  • Software measurements:definition of softwre management metrics and measurement methods
  • Agile and lean software development methods and practices research on effective adoption and use of agile and lean software development methods and practices in software teams and organizations
  • Application of complex adaptive systems to organizing software development processes- research on the application of basic concespts and principles of complex adaptive systems in the organization of software development
  • Innovation in software business - Ideation and innovation processes in the early phase of software development, focusing on the context of software startups
  • Corporate innovation initiatives and strategies, internal startups and aligment
  • Human aspects (affects, emotions, moods, performance, etc.) in empirical software engineering with psychological measurements
  • Modeling, verification and analysis of data-centric and artifact-centric business processes
  • Data-centric declarative distributed computing
  • Social commitments for complex business interaction
  • Data quality for linked open data
  • Process-aware business intelligence
  • Management od business processes and data
  • Graph-structures data management
  • Information integration in cyber-physical systems
  • Knowledge driven information access
  • Databases and ontologies
  • Intelligent conceptual modelling of information systems
  • Designing knowledge base systems
  • Semantic web technologies
  • Efficient quering of data under temporal constrains
  • Entily and aspect diversification in search results
  • Ranking models
  • Opinion mining
  • Mobile software engineering
  • Data mining of open source systems
  • Software reliability and testing
  • Internet-based software engineering
  • Energy-aware software systems
  • Mobile and embedded systems

Doctoral candidates can furthermore specify their own topics together with their designated supervisor, as long as they remain related to the research areas outlined above.  


Career Prospects  

The current Information Technology job market requires specialists, advisors and researchers with technical training in the field who are capable of working in a competitive international field. The goal of the doctorate programme is to train scientists with solid expertise as researchers and specialists to work both in the academic field and in the IT sector. A short overview of some career possibilities for specialists with a Doctorate in Computer Science follows below:
  • University Researcher/Professor: Understanding of scientific topics, both in theory and practice, as well as the ability to find independent ideas and solutions for scientific problems are critical to this job description. The capacity of scientific communication, both written and verbal, is fundamental for high quality instruction and effective dissemination of research findings. In addition, a Doctor of Computer Science must take on a responsibility for the organisation of research projects in the field, for activity planning and coordination of work by students and researchers.
  • Researcher in the IT Sector: A Doctor of Computer Science, as in an academic setting, will find high demand for his or her technical knowledge and expertise in scientific research, communication and the organisation of research work.
  • Business Consultant, particularly in the IT Sector: In this field, the Doctor of Computer Science makes use of his or her training in problem solving, even in fields outside the purely technical ones. The Doctor of Computer Science plays a crucial role in finding means to solve problems in areas lacking structure. In such cases, a problem-solving approach must be developed for each individual case, relying on advanced modelling techniques.
  • Management in the IT Sector: As an industrial manager, the Doctor of Computer Science performs complex tasks in project activity in the field of IT consultation. In addition to management qualities, technical expertise is necessary in order to guide decision-making in the proper direction and to solve problems in various areas.
  • Entrepreneur in the IT Sector: In this field, the Doctor of Computer Science uses his or her ability to work independently, as well as to co-ordinate and manage others’ work. Comprehensive technical expertise in data processing provides inestimable advantages over the competition.  


Faculty of Computer Science - Secretariat
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