The aim of the Master¿s programme in Simulation and Visualization is to educate innovative engineers who can apply their expertise in simulation and visualization to solve complex problems in their own field of expertise.

In the programme, the students develop readily applicable skills in a range of powerful methods for computer simulation in order to solve complex problems within ones own field of competence, and with direct relevance to industry. They learn to conduct simulation experiments, including system modelling, implementation of algorithms, running simulations, interpretation of results, and, importantly, visualization of simulation results.

Topics throughout the Master¿s program are taught from low-level (theoretical understanding and low-level programming) to high-level (practical application to cases from industry and use of existing software). This is to ensure that the students get a thorough understanding of the taught methods, and also are able to apply the methods in order solve complex problems.

The master's programme in simulation and visualization has the following learning objectives at programme level:

Learning Outcome - Knowledge:

• K1 - The candidates shall have advanced knowledge of a wide range of methods for simulation, visualization and artificial intelligence, and shall have good knowledge of cases in which these methods are applicable.
• K2 - The candidates shall have specialized insight in the chosen topic of their master¿s project, within simulation, visualization, and/or related methods in artificial intelligence and/or software architecture.
• K3 - The candidates are able to identify sources of error in modelling and simulation, and have advanced understanding of the methods¿ possibilities and limitations.
• K4 - The candidates have advanced knowledge about scientific literature and methods related to current themes in simulation and visualization.

Learning Outcome - Skills:

• F1 - The candidates are able to analyse existing methods in simulation and visualisation
• F2 - The candidates are able to analyse, and critically evaluate different sources of information, and use them to structure and formulate scientific reasoning
• F3 - The candidates are able to independently analyze complex problems within their own engineering discipline, by applying methods from simulation, visualization and artificial intelligence.
• F4 - The candidates are able to conduct a confined independent simulation experiment under supervision, including system modelling, development and implementation of algorithms, running simulations, visualization and interpretation of results, in accordance with current ethical norms.
• F5 - The candidates are able to work independently with a research project, which implies finding a research question / hypothesis, using relevant scientific theory and methods, setting up and conducting an experiment, and analysing the results.

Learning Outcome - Competence:

• G1 - The candidates are able to choose their simulation and visualization methods in a considered way, and keep a critical as well as ethical attitude during their work.
• G2 - The candidates are able to contribute to innovation in simulation and visualization.
• G3 - The candidates are able to reflect on and communicate about scientific problems, analysis and conclusions within simulation and visualization, both with specialists and the general public.
• G4 - The candidates are able to apply their knowledge and skills in simulation and visualization to new cases in science and industry

• K1 (Foundation): Mathematical, technological, and computer-technical knowledge basis for method understanding, designing, implementing, and evaluating applications in simulation and visualization.
• K2 (Basis): Broad engineering and research-based knowledge of theories, methods, and techniques in simulation and visualization.
• K3 (Research-based expertise): In-depth knowledge within a limited field linked to active research in academic and/or industrial settings including enough insights to develop and apply new research results in simulation and visualization applications.
• K4 (Non-technological subjects): Insights into selected transferable skills such as collaborative teamwork, problem solving, communication and other non-technical subjects of relevance to the engineering profession, and as a basis for developing a broad perspective on the role and engineering challenges in simulation and visualization.

• G1 (Society, Ethics, Sustainability): The ability to understand the role of simulation and visualization in a holistic societal perspective, have insight into ethical requirements and considerations of sustainable development, and be able to analyze ethical issues related to engineering work.
• G2 (Innovation, Multidisciplinary and Multidisciplinary Innovation): The ability to contribute to innovation and entrepreneurship and collaborate with specialists and the general public in multidisciplinary and multidisciplinary innovation processes in simulation and visualization.
• G3 (IT in a societal perspective): The ability to understand the possibilities and limitations of using simulation and visualization technologies, including ethical and legal aspects, as well as societal needs.
• G4 (Leadership and Internationalization): The ability to lead, motivate, and interact with national and international peers in their professional activities.

• F1 (Comprehensive Problem Solving): The ability to define, model, and analyze composite engineering problems in simulation and visualization, including selecting relevant models and methods, and analyze, build, and evaluate solutions independently and critically, in relation to both technical and non-technical factors.
• F2 (Comprehensive and interdisciplinary): The ability to develop comprehensive solutions to simulation and visualization problems, including being able to develop solutions in an interdisciplinary context, and carry out an independent, limited engineering research or development project in simulation and visualization under supervision.
• F3 (Ability to define and conduct research): The ability to work independently with research projects, which implies defining research questions/hypotheses using relevant scientific theory and methods; setting up and conducting experiments; and analyzing and communicating results.
• F4 (Ability to renew and restructure): The ability to independently identify, learn, and use new methods and technologies in simulation and visualization tasks and has a good basis for long-term professional renewal and restructuring.