This module serves manifold competences: As a doctoral consortium it aims at deepening both, methodological and professional research skills of the students. In addition, it is designed to foster the international profile of their work.

By means of submitting their work to an internationally reknown doctoral consortium the students learn how to position themselves in a highly competitive environment.

Since successful applications are invited to be discussed by a selective academic committee the doctoral consortium also serves to further develop the communicative and social competences of the students.

Students' participating in a doctoral consortium arranged in the context of an internationally well-regarded information systems conference, therein share both the main contents of and progress in their own researches. Moreover, they benefit from listening to the other students' experiences and results and receive valuable feedback of the consortium chairs and student participants.

Students will be assisted by lecturers of the Institute of Information Systems at the University of Liechtenstein in preparing their proposal and application for the doctoral consortium. Accepted students will take part in the consortium. Also a reflection of the lessons learned at the consortium together with the lecturers at Liechtensetin is part of the module.

The primary objective of the doctoral consortium is to present and defend the PhD students' research in a competitive and international scientific environment.

The students will be assessed in this module through:

• Competitive Selection Process of the docotral consortium
• Research paper submitted to the doctoral consortium
• Assessment by the Professors discussing the proposal at the doctoral consortium

Module availability:
On application at an internationally renown doctoral consortium, e.g. at ICIS, International Conference on Information Systems; ECIS, European Conference on Information Systems; AMCIS, American Conference on Information Systems or ACIS, Australasian Conference on Information Systems.

This module serves manifold competences: As a doctoral consortium it aims at deepening both, methodological and professional research skills of the students. In addition, it is designed to foster the international profile of their work.

Students' participating in a doctoral consortium, therein share both the main contents of and progress in their own researches. Moreover, they benefit from listening to the other students' experiences and results and receive valuable feedback of the supervisors, consortium chairs and student participants.

The primary objective of the doctoral consortium is to present and defend the PhD students' research in a competitive and international scientific environment.

The students will be assessed in this module through:

• Competitive Selection Process of the doctoral consortium
• Research paper submitted to the doctoral consortium
• Assessment by the Professors discussing the proposal at the doctoral consortium

Module availability:
On application at an internationally renown doctoral consortium.

The doctoral consortium is an opportunity to sharpen and deepen both focus and methods of research, supervised by a group of external professors and/or advisors. It is designed to foster the presentational, critical and discursive skills in a group of international peers. By submitting their work-in-progress and interim results to an international doctoral consortium candidates also learn how to position their work in a competitive research environment.
Applications are refereed through an academic committee. Participants benefit from understanding others' experiences and results and receive valuable feedback from consortium chairs and other participants. A joint publication is to result from a consortium. A doctoral consortium will take the form of a multi-institution and often international seminar, workshop or summer school - an example can be found on www.dokonara.org, the consortium on sustainable spatial development our University participates in each year.

Students will be assisted by lecturers of the Institute of Architecture and Planning at the University of Liechtenstein in preparing their proposal and application for their doctoral consortium participation. Insights acquired will be discussed and reinforced by your doctoral supervisor and other advisors at the University of Liechtenstein.

The primary objective of the doctoral consortium is for the candidates' doctoral research to be presented, argued and advanced in an international and scientific setting.

Successful participation will strengthen research content, method and students' ability to engage in focused, critical discourse.

The students will be assessed in this module through:

• competitive selection process of the doctoral consortium
• research paper submitted to the doctoral consortium
• assessment by the Professors reviewing proposals at the doctoral consortium

Module availability:
In coordination with supervisor: upon application at an international doctoral consortium, e. g. international doctoral research workshops, seminars or symposia on architectural design theory; low-carbon building technology and building integrated sustainability systems; or sustainable spatial development, urban and regional planning and design, or an approved related field.

Im WS 2019/20 wird das Thema "Landschaft" im Mittelpunkt des Moduls stehen. Alles ist Landschaft. Wie wir wohnen, wie wir essen, welches Mobilitätsverhalten wir an den Tag legen, welche planungspolitischen Prioritäten wir setzen und welche Werte wir Gebautem und Freiraum beimessen, bestimmt die Qualität der Landschaft. Sie ist ein Kollektivgut, das es gemeinsam zu bewahren, auszuhandeln und weiterzuentwickeln gilt.
Wir beschäftigen uns innerhalb des Moduls mit dem Perspektivenwechsel, der sich v.a. im europäischen Kontext vollzieht: die Siedlungsentwicklung nach innen von der Landschaft aus zu denken. Auf welche theoretischen Grundlagen kann dieses Denken sich beziehen, welche planungspolitischen und planungspraktischen Konsequenzen bringt es mit sich?

Teilnahme an den Veranstaltungen des Moduls
Selbststudium zu Thema Raumentwicklung und Landschaft
mündliche und schriftliche Präsentation der eigenen Position zum Thema

Die Studierenden erhalten einen Überblick über die aktuelle stadt- und landschaftstheoretische sowie planungspolitische Diskussion zu einer landschaftsorientierten Siedlungsentwicklung nach innen. Die Studierenden sind fähig, die verschiedenen Positionen kritisch zu reflektieren und zu ihrer eigenen Forschung in Beziehung zu setzen. Die Studierenden können eine eigene theoretische Position zum Thema formulieren.

Eine Literaturliste wird in der Kick-off Veranstaltung ausgegeben.

Teilnahme an der Kick-Off Veranstaltung und der Endpräsentation, sowie weiteren Zwischenbesprechungen in Absprache mit der Dozierenden.
Verfassen eines Artikels (3-5 Seiten), in dem das Thema landschaftsorientierte Siedlungsentwicklung nach innen reflektiert und eine eigene Position gefunden wird.

Risk Management:

• Identification, measuring and controlling financial risks.
• Classes of Risk
• Hedging strategies
• The risk management process

Financial Institutions:

• Importance and roles of the main actors on capital and financial markets, basic knowledge of finance intermediation, regulation of banks.
• Financial Intermediation
• Bank regulation

Research Seminar

• Understand and analyze topics in financial services in one of the three offered specialization parts (Finance, Law or Tax).

Voraussetzung für die Anmeldung zum Modul:

• erfolgreicher Abschluss von English I
• erfolgreicher Abschluss von weiteren Modulen des 1. Regelstudienjahres im Umfang von weiteren 45 Credits.

Leben und Lernen in Liechtenstein: mit der Introduction Week möchten wir Sie an der Universität Liechtenstein willkommen heissen und Sie beim erfolgreichen Einstieg ins Leben und Lernen in Liechtenstein unterstützen.

- andere internationale Studierende und das Team des International Office kennen lernen
- sich mit den wichtigsten Ansprechpersonen, Service-Funktionen und organisatorischen Abläufen an der Universität Liechtenstein bekannt machen
- die Umgebung der Hochschule erkunden und wichtige Institutionen kennen lernen
- Informationen über Liechtenstein und Studieren in Liechtenstein erhalten
- Deutsch als Kommunikationsmedium einsetzen
- erste Eindrücke von Kultur und Traditionen in Liechtenstein und seiner Umgebung erleben

Die Nachmittagsaktivitäten werden Ihnen dabei helfen, sich schnell in Liechtenstein einzuleben, Deutsch als Kommunikationsmedium erfolgreich einzusetzen und die Inhalte des Deutschunterrichts praktisch anzuwenden.

Voraussetzung für 2 ECTS sind:
- Teilnahme an allen Veranstaltungen der Introduction Week
- Aktive Mitarbeit während der Veranstaltungen
- Vor-und Nachbereitung ausserhalb des Unterrichts

Ab Semesterbeginnwird der Deutschunterricht wöchentlich auf drei Niveaus mit 2 Unterrichtseinheiten fortgesetzt (Deutsch als Fremdsprache Elementarstufe, Aufbaustufe oder Fortgeschrittenenstufe, jeweils 3 ECTS Credits). Bitte melden Sie sich rechtzeitig für die Lehrveranstaltung an.

This course is designed to give first year PhD Students an aid for their academic endeavour. Just like in Research Design, the focus lies on methodological competences. At the same time, however, this course also aims at techniques rather than design strategies. The objective is to provide core compentences on how to craft a scientific text properly. Due to the concept of peer-monitoring applied in this course also social competencies will be trained.

During the first year students will be working on their academic writing style, they will be made familiar with normative writing styles and ways to publish tackling various kinds of genres, and they will help and learn from each other through peer-monitoring activities. As a base sample texts will be used and the texts students will be producing will be worked on. The course is built on three pillars:

• Knowledge Management:
  Working with databases, literature management softwares, etc.
• Publishing:
  How to write and publish various genres: abstracts, research papers, articles, data commentaries, reviews, project proposals, formatting, etc.
• Peer-Mentoring:
  Giving and receiving feed-back, presenting and reviewing, considering peer-feedback, joint writing activities, etc.

Workshops, one-on-one and think-pair-share sessions, individual and guided e-learning.

Students will be acquainted with principles of academic writing, normative writing, publishing, and peer-mentoring.

By the end of the course they will be able to make use of academic vocabulary, they will be able to discuss texts, tables, charts, and figures, and they will be sensitised about their personal and academic command of the English language.

They will be familiar with reference management systems, working with databases, formatting written texts, and academic values.

They will know principles of certain academic genres, like abstracts, research papers, articles, data commentaries, reviews, project proposals, etc.

They will be able to give and consider peer-feed-back, present and review, and they will be able to carry out joint writing activities, etc.

Natalie Reid (2010). Getting published. Writing strategies for European social scientists. Nova, Oslo. Chapter 4-11

• identify exemplary A-journal papers of your field
• provide own texts for review
• prepare and present reviews
• participate in text discussions
• prepare and present learning input

In the defence the doctoral students prove whether they have fulfilled the requirements of the dissertation.

The defence can be held when the dissertation has been recommended for acceptance in the written appraisal of the supervisors and each supervisor has awarded a minimum grade of 4.0.

The Doctoral Examination Board holds the defence and determines which grade is awarded.

Short description
The course covers various statistical techniques for making sense of the vast and complex data sets that have emerged in business in the past twenty years. Students will learn to detect patterns in large data sets of various formats (quantitative and qualitative) and translate them into actionable insights.

Topics

• Data Visualization and Exploration
• Supervised learning techniques for regression (e.g. linear regression)
• Supervised learning techniques for classification (e.g. classification trees)
• Unsupervised learning techniques (e.g. clustering, dimensionality reduction)
• Deep Learning Fundamentals
• Text mining (e.g. topic modeling)
• Hands-on labs with Python

The module integrates theoretical knowledge and practical skills in an interactive lecture. The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

• Students will know and understand the basic concepts and methods of data mining and predictive Analytics
• Students will assess the assumptions and quality of statistical models
• Students will select and apply the right statistical models for a given task or data set
• Students will derive actionable insights from statistical results

Compulsory reading

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. With Applications in R. New York: Springer (a free online version is available at http://www-bcf.usc.edu/~gareth/ISL/)

Further reading

• Provost, F. & Fawcett, T. (2013). Data Science for Business. Sebastopol: O'Reilly Media

Written exam (90min)

Short description
The course covers the important aspects of modern data management, from design and querying to transaction processing and from traditional to present-day data-driven applications. Students will learn how to handle various data formats, assess and eventually improve data quality, and handle data using SQL, NoSQL, and Hadoop technologies. The course will also look into the basics of mining (big) data sets.

Topics

• Modern data management requirements
• Database system architecture
• Database design using the ER model
• Relational databases (SQL)
• Concurrency control techniques
• NoSQL databases (e.g., MongoDB)
• Apache Hadoop (HDFS, MapReduce)

• The module integrates theoretical knowledge and practical skills in an interactive lecture.
• Selected sessions will also require preparation of the participants through videos that will be provided in advance.
• The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.

• Students will acquire and understand foundational concepts and methods of modern data management
• Students will study the preparation of data in order to enable data-driven applications
• Students will select and apply appropriate technologies for building data-driven applications

Compulsory reading:

• Lemahieu, W., Vanden Broucke, S. & Baesens, B. (2018). Principles of Database Management. Cambridge, UK: Cambridge University Press. ISBN 978-1-107-18612-5 (http://www.pdbmbook.com/)

Written exam (90min)