Доктор наук по Информатике и Математике

Area: Computer Science and Mathematics

• Artificial Intelligence and Intelligent Systems
• Computing and Mathematics
• Cybersecurity
• Data Science and Machine Learning
• Embedded Systems and IoT
• Process Management
• Software and Information System Engineering

Artificial Intelligence and Intelligent Systems

• Adaptive control and optimization
• Adaptation policies
• Collective and adaptive systems
• Knowledge representation and autonomic reasoning

Computing and Mathematics

• Algebraic and logical aspects of computing)
• Computational Models and Models of Computation
• Formal languages
• Algebraic languages for specific domains
• Model theory
• Methodological aspects of science education

Cybersecurity

• Access Control and Resource Usage Policies
• Distributed Ledger Technologies
• Design of Resilient Systems
• Network security
• Intrusion Detection Systems

Data Science and Machine Learning

• Data-driven models of complex systems
• Topology-based data analysis methods
• Data-driven system verification
• Optimization and econometric techniques
• Discriminative and generative modeling

Embedded Systems and IoT

• Middleware and services
• Communication and networking for wireless and embedded systems
• Specifica, progettazione e analisi, e verifica di sistemi embedded distribuiti
• Wireless Sensor Networks
• Energy efficiency and harvesting

Process Management

• Continuous improvement of Process-Aware Information Systems
• Collaborative and Distributed Business Process Enactment including Blockchain
• Models for Process Intensive Systems
• Process Mining Techniques
• Simulation and Verification of Data-Aware Collaboration Models

Software and Information System Engineering

• Engineering of Enterprise Architecture
• Applied Formal Methods and Verification Tools
• Testing and Verification in a distributed setting
• Quantitative Analysis and Tools
• Runtime Monitoring and Verification

The International School of Advanced Studies

UNICAM has instituted an International School of Advanced Studies with the objective of increasing the internationalization of Doctoral education.

• The International School of Advanced Studies plans, organizes, coordinates the courses to achieve the title of Ph.D.
• The Doctoral course is three years with a final thesis to be written in English, that will be defended in front of a Final Examination Board, made up of professors in the relevant scientific areas. This must include academics and other experts (e.g. from research centers or enterprises), most of whom not from the University of Camerino, and at least one non-Italian.
• In addition to the preparation of the final thesis, doctoral candidates are expected to participate in training activities organized by the SAS, all in English, to acquire transferable skills, (such as communications skills, research career development, research management and project funding, methodological skills) and doctoral training in cooperation with industry and other relevant employment sectors.
• Conducting some research in public or private laboratories external to the host University is strongly encouraged.
• To attain the final qualification there should be at least six months experience at a research or higher education Institution different from the University of Camerino and possibly not Italian.
• The Seven Principles for Innovative Doctoral Training underpin our research programs.

Seven Principles for Innovative Doctoral Training

1. Research Excellence

Striving for excellent research is fundamental to all doctoral education and from this, all other elements flow. Academic standards set via peer review procedures and research environments representing a critical mass are required. The new academic generation should be trained to become creative, critical and autonomous intellectual risk-takers, pushing the boundaries of frontier research.

2. Attractive Institutional Environment

Doctoral candidates should find good working conditions to empower them to become independent researchers taking responsibility at an early stage for the scope, direction, and progress of their project. These should include career development opportunities, in line with the European Charter for Researchers and the Code of Conduct for the Recruitment of Researchers.

3. Interdisciplinary Research Options

Doctoral training must be embedded in an open research environment and culture to ensure that any appropriate opportunities for cross-fertilization between disciplines can foster the necessary breadth and interdisciplinary approach.

4. Exposure to industry and other relevant employment sectors

The term 'industry' is used in the widest sense, including all fields of future workplaces and public engagement, from industry to business, government, NGO’s, charities and cultural institutions (e.g. musea). This can include placements during research training; shared funding; involvement of non-academics from relevant industry in informing/delivering teaching and supervision; promoting financial contribution of the relevant industry to doctoral programs; fostering alumni networks that can support the candidate (for example mentoring schemes) and the program, and a wide array of people/technology/knowledge transfer activities.

5. International networking

Doctoral training should provide opportunities for international networking, i.e. through collaborative research, co-tutelle, dual and joint degrees. Mobility should be encouraged, be it through conferences, short research visits, and secondments or longer stays abroad.

6. Transferable skills training

“Transferable skills are skills learned in one context (for example research) that are used in another (for example future employment whether that is in research, business, etc.). They enable subject- and research-related skills to be applied and developed effectively. Transferable skills may be acquired through training or through work experience”. It is essential to ensure that enough researchers have the skills demanded by the knowledge-based economy. Examples include communication, teamwork, entrepreneurship, project management, IPR, ethics, standardization, etc.

Business should also be more involved in curricula development and doctoral training so that skills better match industry needs, building on the work of the University Business Forum and the outcomes of the EUA DOC-CAREERS project. There are good examples of interdisciplinary approaches in universities bringing together skills ranging from research to financial and business skills and from creativity and design to intercultural skills.

7. Quality Assurance

The accountability procedures must be established on the research base of doctoral education and for that reason, they should be developed separately from the quality assurance in the first and second cycle.

The goal of quality assurance in doctoral education should be to enhance the quality of the research environment as well as promoting transparent and accountable procedures for topics such as admission, supervision, awarding the doctorate degree and career development. It is important to stress that this is not about the quality assurance of the Ph.D. itself rather the process or life cycle, from recruitment to graduation.