MASYK4000 Introduction to Clinical Research and Professional Development Course description

In the course, the students will acquire knowledge and competence related to clinical research and professional development, including in critical assessment of nursing practice. Key areas of nursing research will be elucidated and the consequences of different concepts and forms of knowledge analysed on the basis of clinical practice. The relationship between theory and empirical data will be explored and discussed by examining different positions in science and research.

Students will acquire knowledge of the main approaches to evidence-based practice, and the relationship between clinical experience, user participation and research will be discussed. The course emphasises the development of basic skills in literature searches, critical assessment of sources and academic writing and argumentation.

The course also addresses educational and organisational challenges relating to the implementation of new practices on the basis of research and professional development in the clinic. This includes highlighting key functions such as supervising and teaching colleagues and students, as well as initiating and leading professional change processes.

Admission to the programme.

After completing this course, the students have the following learning outcomes, defined in terms of knowledge, skills and general competence:

Knowledge

On successful completion of this course the student has:

• an understanding of the theoretical foundations of operative cybersecurity

• knowledge of offensive and defensive cybersecurity measures
• awareness of reference databases for vulnerabilities, exploits and information security advisories
• knowledge of professional ethics in cybersecurity and penetration testing
• knowledge of legal limitations for cybersecurity activities
• familiarity with international, national and sectorial crisis response and cybersecurity authorities

Skills

On successful completion of this course the student can:

• gather information (reconnaissance) about target systems and target organizations, identify vulnerabilities and choose targets for penetration testing
• run penetration testing with practical attacks against systems, software and users
• detect and identify intrusion to systems and execute countermeasures

• retrieve current intelligence about vulnerabilities, security patches and attack methods
• distinguish risk-based approach to cybersecurity operations from ad hoc cybersecurity operations
• configure intrusion detection or endpoint detection and response (EDR) agents
• configure and collect logs
• detect and monitor intrusions
• recover and secure evidence from log files and other resources for analysis of events
• find, exploit and mitigate vulnerabilities in networked information systems.

General competence

On successful completion of this course the student can:

• organize cybersecurity operations

• use relevant tools for cybersecurity operations
• use relevant tools for passive and active cybersecurity operations
• apply their knowledge of general incident management

Lectures, group work, seminars with assignment presentations and self-study.

The following must have been approved in order for a student to be permitted to take the exam:

Assignment: Clinical research and professional development

• Written assignment in groups of 3¿4 students
• Scope: 2,500 words (+/- 10%)
• Oral presentation of the assignment to fellow students and lecturer(s)
• Oral feedback from fellow students
• Oral feedback from lecturer(s)

Documentation of literature selected by the student: 200 pages.

Individual written home exam over 3 days.

Scope: around 2,500 words (+/- 10%)

All aids are permitted, as long as the rules for source referencing are complied with.

Grade scale A¿F.

This course will present the state of the art in algorithms for machine learning for the 3D environment. We will cover topics related to deep learning for 3D data such as point clouds, multi-view images, and shape graphs. The course covers applications like classification, segmentation, shape retrieval and scene representation.

No formal requirements over and above the admission requirements.