• VT-Gesetz Liechtenstein
• Regulierung von Blockchain-Dienstleistern im EWR und in Drittstaaten
• Blockchain-Regulierung und Finanzmarktrecht
• Buchhalterische Besonderheiten bei Kryptosachverhalten:
• Deklaration und Besteuerung von Kryptowährungen bei natürlichen Personen
• Besteuerung von krypto- und blockchainbasierten Unternehmen in FL
• Mehrwertsteuerliche Behandlung eines ICO/TGE/STO
• Grenzüberschreitende Steuerplanung

• Wissen und Verstehen:

Die Studierenden wissen, welche Dienstleistungen, die auf der Blockchain angeboten werden, regulatorisch erfasst werden und welche Verhaltenspflichten Dienstleister, die Geschäftsmodelle auf der Blockchain anbieten, im EWR und in Liechtenstein einhalten müssen. Ferner sind die Studierenden befähigt, die angesprochenen Regelungen von anderen Gesetzen, unter anderem im Finanzmarktrecht, abzugrenzen. Die Richtlinien für die Besteuerung von Unternehmen und natürlichen Personen sind bekannt.

• Anwendung von Wissen und Verstehen:

Die Studierenden sind in der Lage, neue und bestehende Geschäftsmodelle anhand der geltenden Regelungen des EWR-Rechts zu analysieren und Aussagen über das rechtliche Risiko der Geschäftsmodelle zu treffen, etwa, ob ein Geschäftsmodell bewilligungspflichtig ist oder ohne Bewilligung im EWR bzw in Liechtenstein ausgeübt werden kann. Die steuerlichen Richtlinien können auf die gängigsten Geschäftsmodelle angewendet werden.

• Urteilen:

Die Studierenden sind fähig, die rechtlichen und steuerlichen Chancen und Risiken von Dienstleistungen, die via Blockchain angeboten werden, zu bewerten.

• Kommunikative Fertigkeiten:

Die Studierenden sind fähig, in Zusammenarbeit mit Aufsichtsbehörden, Steuerberatern und Projektpartnern Geschäftsmodelle und ihre regulatorischen Risiken darzustellen und zu diskutieren.

• Selbstlernfähigkeit:

Die Studierenden können auf Basis der erworbenen rechtlichen Kenntnisse selbständig neue Geschäftsmodelle erarbeiten und bestehende Geschäftsmodelle auf ihre rechtlichen Risiken hin analysieren.

This optional module allows for various kinds of research studies. It is closely connected to the five units of the Liechtenstein School of Architecture and is usually part of ongoing research projects. The supervision consists of directing the students towards clear results within a given field of research. The individual study is reviewed within the respective unit.

Built Heritage & Upcycling Unit:
Whenever there was a shortage of building materials in history, creativity was called for and a circular approach was often the answer. In the 16th century, the Frenchman Philibert de l'Orme invented a new construction method, known as "à petit bois", in which large roof surfaces were built from small pieces of wood. Instead of comprehensive designs, his architectural work was characterized by adapting, integrating and building on. We examine de l'Orme's circular approaches using the example of the roof truss of the Caserne Rochambeau in Mont-Dauphin (F). Could this method be one of the earliest concepts for planned circular building solutions?

Self-defined design or research studies, developed individually or in groups agreed upon with research units and under the guidance of mentors. The size of the module is determined by the respective unit.

After successful completion of the course, students will be able to

Relevant reading will be made available at the beginning of the course. A list of recommended literature will be announced in the course and updated on an ongoing basis.

Minimum 75% compulsory attendance, regular meetings with instructors, continuous assessment, portfolio and final review.
The final grade is calculated according to the weighting of the following components: final submission (80%) and oral presentation (20%).

Individual appointments will be set with the tutor.

Group projects are also possible, as well as group work with individual submissions.

The purpose of this course is to familiarize students with the statistical methods and tools necessary not only for producing high quality research output in finance, but also necessary to understand and apply the quantitative tools that are at the core of a modern and innovative financial business. In this context , students will recapture common statistical concepts such as regression analysis and hypothesis testing within financial data as well as the basics of linear algebra. Simultaneously, students will learn how to use R, a statistical software that has become
standard in research and industry. A third competence gained during the course is about how to find and download data from professional (Refinitiv) and open-source data providers.

Key topics covered are:

• Statistical programming in R (tidy data handling, programming)
• Basics of linear algebra (vectors, matrices, systems of linear equations)
• Descriptive statistics for uni- and multivariate analysis
• Time series analysis
• Hypothesis testing
• Regression analysis: uni-/ multivariate, (non-)linear

• The course is a combination of interactive lectures and coding sessions.
• Practical exercises and hands-on coding using R.
• Case studies and real-world data analysis projects.

After successful completion of the course, students will

Professional competence

• conduct empirical analysis of financial data using R.
• manage large financial datasets efficiently.
• understand and apply statistical methods to real-world financial data.

Methodological competence

• apply simple and multiple linear regressions to financial data, including model specification, estimation, and interpretation.
• perform diagnostic tests for regression models, such as tests for heteroscedasticity, multicollinearity, and autocorrelation.
• address common issues in financial time series data.
• apply statistical methods like hypothesis testing and non-parametric tests to financial datasets.

Technological competence

• develop proficiency in statistical programming in R, with a focus on the tidyverse.
• efficiently manipulate and visualize data using R.
• extract and handle data from professional and open-source data providers.

• Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model

Data. O'Reilly Media.

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning: with Ap-

plications in R. Springer.

• Students are provided with the lecture slides and supplementary material (e.g., selected journal articles).

Written exam (70%), Online programming exam (30%); Attendance is mandatory
(80%)

This optional module allows for various kinds of research studies. It is closely connected to the five units of the Liechtenstein School of Architecture and is usually part of ongoing research projects. The supervision consists of directing the students towards clear results within a given field of research. The individual study is reviewed within the respective unit.

Urban Design & Spatial Development Unit:
We are happy to support your independent study projects that use our methods and approaches to pursue relevant urban and spatial development issues and to help you find your own research questions. We can offer topics for independent studies related to our research areas in the Alpine Rhine Valley and beyond.
Then get in touch with us. We look forward to working with you!

Self-defined design or research studies, developed individually or in groups agreed upon with research units and under the guidance of mentors. The size of the module is determined by the respective unit.

After successful completion of the course, students will be able to

Relevant reading will be made available at the beginning of the course. A list of recommended literature will be announced in the course and updated on an ongoing basis.

Minimum 75% compulsory attendance, regular meetings with instructors, continuous assessment, portfolio and final review.
The final grade is calculated according to the weighting of the following components: final submission (80%) and oral presentation (20%).

Individual appointments will be set with the tutor.

Group projects are also possible, as well as group work with individual submissions

For registration and enrolment, please get in touch with Michael Wagner directly: michael.wagner@uni.li (or any member of the unit)

Advanced ISD project is a course wherein students must put their pre-acquired knowledge (in the Information Systems Development and Web-based Information Systems) into practice. After providing a brief introduction on some web frameworks and on API, students are meant to work in groups to develop a Web-based Information System.

• Two lectures at the beginning of the course will cover some basic concepts of Web development (in Py-thon)
• Most of the work is meant to be done by groups of 2-5 students

After successful completion of the course, students will

Professional competence
• Learn how to leverage existing frameworks to develop a web-based Information System
• Learn how to develop “original” Information Systems
• Learn how to work in groups to develop an Information System
• Learn how to document self-developed source code of an Information System

Methodological competence
• have the capability of developing and deploying functional web-applications
• have the capability of debugging self-produced pieces of software code

Social competence
• have the capability of working in groups with a common goal

Personal competence
• have the capability of autonomously troubleshooting the problems occurring in the most common IT op-erations
• have the capability of autonomously searching for the relevant information required to accomplish a given task

Technological competence
• have the capability of using popular web-development frameworks (e.g., Flask, Django)
• have the capability of leveraging public API

• Students are provided with the lecture slides and supplementary material (e.g., selected journal articles).
• “Rest API Design Rulebook”, Mark Masse.

Project results (80%), presentations (20%); Attendance is mandatory (80%)

Aus der Praxis hat sich gezeigt, dass Architektinnen und Architekten in ihrem Schaffen selten alleine agieren. Gestützt auf diese Tatsache orientieren sich die Inhalte des Modules an Aufgaben wie dem Agieren im Team aber auch am adäquaten Umgang mit den Behörden oder externen Büros. Die Studierenden erhalten ein Gefühl dafür was es bedeutet eine erste Vision mit Fachplanerinnen und Fachplanern abzustimmen und wann welche Expertise benötigt wird.

In Form von Vortrag, Projektarbeiten, Übungen,
Recherche, Visualisierung, Peerfeedback, Diagrammen,Grafiken, Skizzen, Zeichnungen und Plänen.

Das Lehrmaterial wird den Studierenden semesterweise zur Verfügung gestellt.

Modulnote = Lehrveranstaltungsnote, die ermittelt wird aus:
Fachprojekt, Übungen, Mitarbeit im Unterricht; 70% Anwesenheitspflicht, prüfungsimmanent

Process Mining provides a comprehensive exploration of the fundamentals of process mining, including conceptual foundations, methods, and technologies used for analyzing business processes with the help of digital trace data recorded in event logs.
Students attending this course will gain knowledge of foundational concepts and algorithms in process mining and acquire practical skills to mine digital trace data using process mining techniques and software. Students will also learn the main steps of conducting a process mining project within an organization as well as common challenges and strategies of process mining analysis.

The course covers four primary topics:

• Petri-net foundations of process analysis
• Process mining algorithms, including process discovery and conformance-checking algorithms
• Process mining project methodologies, strategies, and challenges of process mining analysis
• Process mining tools and applications

• The course involves interactive lectures with exercises and practical sessions with process mining software that allow integrating theoretical knowledge with analytical skills. The practical exercises run in class will allow students to work with process mining hands-on and prepare for the final exam.

After successful completion of the course, students will

Professional competence

• understand how a process mining project can be conducted in practice
• be aware of best practice strategies for conducting process mining analysis
• know how to analyze digital trace data using process mining tools

Methodological competence

• understand the foundational concepts of process mining
• understand how process mining algorithms work
• run process discovery and conformance-checking algorithms

Social competence

• know about the pitfalls of process mining
• understand how process mining is embedded in an enterprise setting

Personal competence

• be able to identify business challenges for which process mining is a fitting solution
• be able to identify challenges of process mining analysis
• be able to apply a process-oriented way of thinking when approaching data science projects

Technological competence

• understand and explain the foundational concepts of process mining
• understand and explain how process mining algorithms work
• be able to use popular process mining tools

• van der Aalst, W. M., & Carmona, J. (2022). Process Mining Handbook. Springer Nature.
• Dumas, M., La Rosa, M., Mendling, J., & Reijers, H. (2018). Fundamentals of Business Process Management (2nd edition). Berlin, Germany: Springer.
• Pointers to additional readings will be provided during the lectures.

Written exam

Climate Design encompasses various aspects aimed at considering the influence of climate in architectural designs. The key concepts covered in the Climate Design seminar are climatic analysis, passive design strategies, energyefficient technologies, sustainable materialisation as well as digital simulation and modelling tools. By mastering these curriculum components, architecture students can design buildings that maximise user comfort while minimising their environmental footprint. Overall, the learning outcomes of studying climate design in architecture include equipping students with the knowledge and skills to create sustainable, energy-efficient, and environmentally conscious designs that prioritise user comfort and reduce the overall ecological footprint of buildings.

1. Understanding of Climate Factors
2. Integration of Passive Design Strategies
3. Application of Energy-Efficient Technologies
4. Selection of Sustainable Materials
5. Use of Simulation and Modeling Tools
6. Environmental Consciousness
7. Effective Communication and Collaboration

The seminar involves interactive lectures with exercises to integrate theoretical knowledge with critical analysis skills. Case studies are used to discuss the course contents. Recent scientific publications from Sustainable Design and climate architecture are examined in class.

After successful completion of the course, students will be able to

Relevant reading will be made available at the beginning of the course. A list of recommended literature will be announced in the course and updated on an ongoing basis.

Minimum 75% compulsory attendance and continuous assessment.
The final grade is calculated according to the weighting of the following components: final submission (80%) and active participation (20%).