Vienna PhD School of Informatics

  • Show all courses of the academic year of which the selected semester is part of.

2019W-2020S (2011U)

TitlePrecon.HoursECTS
Vienna PhD School of Informatics
Fundamental Courses
20.0
VU Philosophy of Science
3.0
195.080 VU 2019W
2.03.0
VU Innovation
3.0
VU Research Methods in Computer Science
3.0
VU PhD seminar
3.0
VU Fundamental research methods for doctoral students
2.03.0
SE Research and Career Planning for Doctoral Students
2.03.0
VU Research and Career Planning for Doctoral Students
2.03.0
2.03.0
VU Being a Researcher
2.03.0
199.086 VU 2019W
2.03.0
Area Courses
21.0
VU PhD Primary Area Computer Engineering Introduction
3.0
VU Foundations of Data and Knowledge Systems
3.0
VU Introduction to Media Informatics and Visual Computing at VUT
3.0
VO Foundations of Business Informatics
3.0
VU Advanced Topics in Service-oriented and Cloud Computing
2.03.0
VU Model Checking
2.03.0
VU Discrete Mathematics and Probability
3.0
VU Formal Methods
3.0
VU Linear Algebra
3.0
VU Algorithms
3.0
VU Shape from function methods
3.0
VU Differential Equations
3.0
VU Computational Geometry and Topology
3.0
VU Computational Complexity
3.0
VU Essence of Cloud Computing
VU Hybrid Systems
2.03.0
VU Hybrid Systems (Supplement)
2.03.0
VU Media Understanding
2.03.0
VU Advanced Concepts in Distributed Systems Research
2.03.0
VU Abstract Interpretation: from theory to applications
2.03.0
VU Generative Software Development
3.0
VU Computational photography and computational imaging
2.03.0
VU Future trends in imaging: mobile, augmented, pervasive and social
2.03.0
VU Machine Learning
3.0
VU Recommender Systems
2.03.0
VU Description Logics, Ontology-based Data Access, and Reasoning
2.03.0
VU Design and Analysis of Quasi-Experiments for Causal Inference
2.03.0
VU Advanced Topics in Web of Data
2.03.0
VU Model Predictive Control
VU Computational Complexity
VU Geometry & Topology
VU Automated Scheduling and Timetabling
2.03.0
VU Data Warehousing and Business Intelligence
2.03.0
VU Introduction to Data Mining: Clustering and Outlier Detection
3.0
VU Fundamental Underpinnings of the Cloud
2.03.0
VU Gröbner Bases: A General Methodology for Many-Variable Non-linear Systems
3.0
VU Automata-theoretic methods for infinite-state verification
2.03.0
VU Social Network Analysis
2.03.0
VU Graph Partitioning and Graph Clustering in Theory and Practice
2.03.0
VU Domain and Requirements: Science & Engineering
2.03.0
VU Model Theoretic Methods in Computer Science: Selected Topics
2.03.0
VU Outlier Detection
2.03.0
VU Bayesian Machine Learning
2.03.0
VU Constructive Reasoning
2.03.0
VU Advances and challenges in vision science
2.03.0
VU Algorithm Design in Different Models of Computation
2.03.0
VU Metamodeling
2.03.0
VU From Web Data to Network Analysis
2.03.0
VU Concurrency Theory
2.03.0
VU Healthcare in domestic settings, a CSCW perspective
2.03.0
VU Quantitative evaluation of concurrent systems with stochastic temporal parameters
2.03.0
VU Engineering Self-Adaptive Systems
2.03.0
VU ProWriting - Effective Research Project Proposal Writing for Public Funding: Write up your research ideas
2.03.0
VU DataFlow SuperComputing for BigData Analytics
2.03.0
VU Real Virtuality: Authentic virtual experiences
2.03.0
VU Design, Creativity, Learning, and Human-Centered Computing - Exploring Foundations and Themes for the Future of the Digital Age
2.03.0
VU Machine Learning Security
2.03.0
VU Talent Engineering - From Purpose to Impact
2.03.0
VU Memory Systems and Memory-Centric Computing Systems: Fundamentals and Recent Research
2.03.0
VU Recommender Systems Team Project
2.03.0
VU Deontic Logic for Normative Reasoning
2.03.0
2.03.0
VU Reachability Analysis Techniques for Hybrid Systems
2.03.0
2.03.0
External courses
2.03.0
VU Summer/Winter School for PhDs
2.03.0

Legend

Courses belong to the introductory and orientation phase ("Studieneingangs- und Orientierungsphase")
Courses belong to the introductory interview ("Studieneingangsgespräch")
Courses require the completion of the introductory and orientation phase
Courses require the completion of the introductory interview STEG