Curriculum

DEIA gives you the interdisciplinary skills to understand, model and develop complex physical systems and enrich them with software, data and Artificial Intelligence.

Modules

Digital Engineering & Mechatronics

	1.	2.	3.
Individual Qualification	4
Individual Qualification Graduates possess an interdisciplinary set of competencies for address-ing technical development tasks in digital engineering and mechatron-ics. They can analyse the interaction of mechanical, electrical, elec-tronic, control and software components, decompose mechatronic sys-tems into functional subsystems and formulate appropriate assump-tions, parameters and boundary conditions for subsequent model-based development. They are able to understand and develop structured Py-thon programs, process numerical and tabular data and use program-ming to support technical analysis, modelling and simulation. Further-more, they can structure, plan, monitor and document interdisciplinary engineering projects in accordance with established project manage-ment principles. By combining mechatronic systems thinking, program-ming and project management, they are prepared to contribute to the analysis and implementation of digital engineering projects. Programming Type: compulsory course, Integrated course ECTS: 2 Exam type: written examination Algorithmic problem-solving and computational thinking; programming with Python for engineering, data and AI-related applications; variables, data types, operators, conditional statements, loops, functions, modules and commonly used data structures; structured decomposition of pro-gramming tasks into reusable components; reading, processing and ex-porting data; use of selected Python libraries such as NumPy, pandas and Matplotlib for numerical computation, data handling and visualisa-tion; debugging and handling runtime errors; implementation of applica-tion-oriented programming tasks based on technical and industrial ex-amples. Advanced Mechatronics Semester: 1 Type: compulsory course, Integrated course ECTS: 3 Exam type: written examination Interdisciplinary analysis of mechatronic systems and the interaction of mechanical, electrical, electronic, control and software components; functional decomposition of mechatronic systems into subsystems and definition of interfaces, energy flows and signal flows; physical modelling of coupled electromechanical systems using component characteristics, conservation laws and system equations; system-level integration and assessment of sensors, actuators, drives, power electronics, embedded controllers and software; analysis of dynamic system interactions, rele-vant non-linearities, uncertainties and technical limitations; formulation of model assumptions, parameters and boundary conditions as prepa-ration for control-oriented modelling; application-oriented case study in-volving the derivation, simulation and validation of a representative mechatronic system model. Project Management Semester: 1 Type: compulsory course, Lecture ECTS: 1 Exam type: written examination Principles of project management based on IPMA standards and termi-nology; project management as a business process and its role in pro-ject-oriented organisations; methods for defining, planning, initiating, ex-ecuting, monitoring, controlling and closing projects; preparation of a project handbook, for example based on the Industrial AI Systems Lab.
Model Based Engineering 1	10
Model Based Engineering 1 Graduates possess an advanced understanding of the interaction be-tween physical systems, sensing, signal processing, industrial commu-nication and control-oriented modelling. They can select suitable sen-sors and measurement methods, acquire, condition and analyse meas-urement data, assess data quality and uncertainty, and design data flows from sensors to controllers and higher-level systems. They are able to derive, abstract and parameterise physics-based models of dy-namic systems, analyse continuous- and discrete-time state-space rep-resentations with respect to stability, controllability and observability, and design state-feedback controllers and observers. Furthermore, they can combine measurement data and system models in simulation-based development workflows, compare simulated and measured sys-tem behaviour, and use the results to analyse and validate mechatronic systems. Model Based Engineering 1 Semester: 1 Type: compulsory course, Integrated course ECTS: 5 Exam type: written examination Principles of control engineering and control-oriented modelling; deriva-tion, abstraction and parameterisation of physics-based dynamic sys-tem models; continuous- and discrete-time state-space models, includ-ing eigenvalue analysis, stability, controllability and observability; design of state-feedback controllers and observers; modelling, simulation and system analysis with MATLAB/Simulink; application-oriented computer and hardware laboratory, including comparison of simulated and meas-ured system behaviour. Sensors, Signal Processing & Industrial Communication Semester: 1 Type: compulsory course, Integrated course ECTS: 5 Exam type: written examination Sensor and measurement technology: physical principles, sensor types and characteristics, calibration, measurement errors and uncertainty; analogue and digital signal processing, including signal conditioning, fil-tering, sampling and time- and frequency-domain analysis; analysis and quality assessment of sensor and measurement data; industrial data ac-quisition and communication from sensors to controllers and higher-level systems, including selected interfaces, protocols and data-pipeline requirements; application-oriented laboratory exercises in measure-ment, signal processing and data transmission, including links to system models.
Model Based Engineering 2		9
Model Based Engineering 2 Graduates possess an advanced understanding of model-based devel-opment and can use models to design, analyse and optimise mecha-tronic products and embedded functions. They can select and apply methods for state estimation, scheduling, optimisation and model-sup-ported decision-making, including Kalman-filter, fuzzy and game-theo-retic approaches, and implement and validate model-based functions in embedded systems. They are able to create numerical simulation mod-els based on ordinary and partial differential equations, apply selected numerical methods, and use Virtual Prototyping to analyse product be-haviour and support design decisions. Furthermore, they can apply model reduction and AI-based surrogate models for efficient simulation and optimisation, critically assess model assumptions and results, and integrate these findings into digital product development processes. Digital Product Development Semester: 2 Type: compulsory course, Integrated course ECTS: 4 Exam type: written examination Digital product development from concept to virtual prototype; Virtual Prototyping and simulation-driven design of mechatronic products; mod-elling and numerical simulation of dynamic and multiphysical systems based on ordinary and partial differential equations; selected numerical solution methods (Finite Element and Finite Volume Methods, Smoothed Particle Hydrodynamics, Arbitrary Lagrange Euler Method); interpretation and use of simulation results for product evaluation and design decisions; model reduction and AI-based surrogate models for efficient design exploration; simulation-based optimisation; application-oriented case studies. Model Based Engineering 2 Semester: 2 Type: compulsory course, Integrated course ECTS: 5 Exam type: written examination Model-based design and implementation of embedded systems; imple-mentation and validation of model-based functions under real-time and resource constraints; state estimation using Kalman filters; scheduling and optimisation methods for technical systems; model-supported deci-sion-making, including group decision-making and game-theoretic ap-proaches; fuzzy logic for modelling, control and decision-making under uncertainty; integration of estimation, optimisation and decision methods into simulation-based development workflows; selected current topics in Model-Based Engineering.
Advanced Digital Engineering			5
Advanced Digital Engineering Graduates possess an advanced understanding of model-based, data-driven and control-oriented methods for analysing, simulating, optimis-ing and controlling industrial systems. They can derive models of tech-nical systems from measurement and process data, estimate model pa-rameters, select appropriate modelling approaches, and assess model quality and uncertainty. They are able to combine physical and data-driven models into hybrid modelling approaches and apply Model Pre-dictive Control to industrial problems while considering optimisation ob-jectives and constraints. Furthermore, they can use neural-network-based models and Physics-Informed Neural Networks for the modelling and simulation of dynamical systems and select suitable model-based and data-driven approaches for optimisation, control and decision sup-port in industrial applications. Model Based Engineering 3 Semester: 3 Type: compulsory course, Integrated course ECTS: 5 Exam type: written examination Advanced Model-Based Engineering methods for industrial dynamical systems; data-driven and physics-based modelling of technical systems; system identification and parameter estimation using measurement and process data; hybrid modelling through the combination of physical models and data-driven methods; Model Predictive Control, including prediction models, optimisation logic, constraints and industrial applications; AI-supported modelling of dynamical systems using neural-network-based models and Physics-Informed Neural Networks; model validation, uncertainty analysis and assessment of model quality; use of model-based and data-driven approaches for simulation, optimisation, control and decision support in production, energy and process systems.

Industrial AI

	1.	2.	3.
Industrial AI Foundations	10
Industrial AI Foundations Graduates possess an integrated understanding of the mathematical, statistical and software engineering methods required to develop indus-trial AI applications. They can prepare and analyse data, select and ap-ply suitable regression, classification, unsupervised learning and en-semble methods, implement them using Python and assess model per-formance using appropriate evaluation metrics. They are able to specify functional and non-functional requirements, design modular and main-tainable software architectures and integrate software components, ser-vices and data sources. Furthermore, they can apply collaborative, agile and DevOps practices as well as systematic testing and quality assur-ance to develop reliable data-driven and AI-enabled solutions from re-quirements analysis through deployment and maintenance. Applied Mathematics & Machine Learning Semester: 1 Type: compulsory course, Integrated course ECTS: 6 Exam type: written examination Application-oriented mathematical, statistical and software foundations of modern AI systems; data analysis, modelling and implementation of industrial AI applications using Python; mathematical methods for AI, particularly statistics and probability theory, linear algebra, calculus, op-timisation, regression methods, and distance and similarity measures; principles of machine learning and data mining; unsupervised learning, linear and non-linear regression models, and classical classification methods; overfitting, underfitting and evaluation metrics for assessing AI models; ensemble methods such as boosting, bagging and stacking; application of these methods to industrial problems. Software Engineering Semester: 1 Type: compulsory course, Integrated course ECTS: 4 Exam type: written examination Software development lifecycle from requirements analysis to deploy-ment and maintenance; specification of functional and non-functional re-quirements; software architecture and design principles for modular, maintainable and extensible engineering, data and AI applications; ob-ject-oriented design, interfaces and integration of software components, services and data sources; collaborative development using version control, documentation and code review; software quality assurance through systematic testing and selected quality measures; agile devel-opment and DevOps practices, including automated build, testing and continuous integration and delivery; application-oriented software devel-opment project in an industrial or digital engineering context.
Industrial AI Applications		8
Industrial AI Applications Graduates possess an advanced understanding of modern Industrial AI applications and can prepare, analyse and model image, sensor, pro-cess, time-series and tabular data for industrial problems. They are able to select and apply appropriate methods from computer vision, sensor data processing, deep learning, ensemble learning and sequence learn-ing to specific industrial use cases and evaluate the resulting models and outputs using suitable quality and performance metrics. Further-more, they can assess emerging approaches such as Generative AI and Agentic AI in terms of their industrial applicability and potential for quality inspection, object detection, process and condition monitoring, anomaly detection and forecasting. Advanced Industrial AI Applications Semester: 2 Type: compulsory course, Integrated course ECTS: 4 Exam type: written examination Advanced Industrial AI applications based on relevant industrial use cases; design, training and evaluation of artificial neural networks and deep-learning models for unstructured data; advanced application of tree-based ensemble methods to tabular data; sequence-learning approaches for time-series analysis and forecasting; Generative AI for natural language processing; autonomous decision-making and Agentic AI; selection and evaluation of suitable methods for different data types and application contexts; integration and critical assessment of emerging AI developments through application-oriented case studies and expert talks with representatives of leading Upper Austrian industrial companies. Computer Vision & Sensor Data Processing Semester: 2 Type: compulsory course, Integrated course ECTS: 4 Exam type: written examination Application-oriented processing of industrial image and sensor data; ac-quisition, calibration, synchronisation and quality assurance of image and sensor datasets; preprocessing, including filtering, normalisation, segmentation, feature extraction and handling of noise, outliers and missing values; classical and data-driven computer vision methods for object and pattern recognition and automated quality inspection; appli-cation of selected deep-learning approaches to industrial image analy-sis; sensor-data processing for process and condition monitoring and anomaly detection; integration of image, sensor and process data; eval-uation using suitable quality and performance metrics; application to in-dustrial use cases.
Intelligent Physical Systems		5	4
Intelligent Physical Systems Graduates possess an advanced understanding of intelligent cyber-physical systems and can integrate physical systems, sensing, actua-tion, embedded systems, industrial IT/OT architectures and AI methods into coherent industrial applications. They can analyse architectures spanning PLC, SCADA, MES, ERP and IIoT, design data flows and in-terfaces across edge, fog and cloud environments, and account for data quality, interoperability, security and safety. Furthermore, they can apply concepts of autonomous systems, advanced robotics, Reinforcement Learning, Digital Twins, virtual commissioning and immersive technolo-gies to industrial tasks. They are able to use virtual models and AI-based methods to develop, train, validate and optimise autonomous and ro-botic systems as well as plant layouts, production processes and system parameters. Cyber-Physical Systems & Physical AI Semester: 2 Type: compulsory course, Integrated course ECTS: 5 Exam type: written examination Development of intelligent cyber-physical systems by integrating sensing, actuation, embedded systems and AI methods for perception, decision-making and adaptation; connection of physical systems with datadriven models, Edge AI and industrial Physical AI applications; industrial IT/OT architectures and integration levels, including PLC, SCADA, MES and ERP; automation pyramid and Industry 4.0 architectures; IIoT devices, platforms and interoperability using selected communication standards such as OPC UA and MQTT; distribution of data processing and AI functions across edge, fog and cloud environments; data management and data quality; security and safety in networked IT/OT systems; application-oriented design and analysis of integrated cyber-physical systems. Autonomous Systems & Advanced Robotics Semester: 3 Type: compulsory course, Integrated course ECTS: 4 Exam type: written examination Development of autonomous systems and advanced robotic solutions; perception, navigation, planning, collaboration and AI-based control; concepts and distinctions between the digital factory, virtual commissioning, Digital Twins and Virtual Mock-ups; integration of autonomous and robotic systems into industrial environments; application of augmented, virtual and mixed reality in industrial contexts; Reinforcement Learning for autonomous decision-making and advanced robotics; development and use of virtual models and Digital Twins as training and validation environments for Reinforcement Learning; virtual plant models for AI-based optimisation of plant layouts, production processes and system parameters; application-oriented industrial case studies.
Responsible Industrial AI			7
Responsible Industrial AI Graduates possess an advanced understanding of the safety, security, regulatory, ethical and organisational requirements for industrial AI sys-tems. They can analyse functional safety and cybersecurity across cyber-physical systems, IT/OT architectures and AI-enabled applica-tions, assess hazards, threats and AI-specific risks, and derive appro-priate protection, validation and integration measures. They are able to account for data quality, model uncertainty, bias, limited traceability, ro-bustness and manipulation risks when designing trustworthy AI sys-tems. Furthermore, they can apply principles of Human-Centric and Ex-plainable AI, data and AI governance and risk-based regulatory compli-ance, particularly under the EU AI Act, and define human oversight, roles, processes and responsibilities for the trustworthy deployment and operation of industrial AI systems. Human-Centric AI & Governance Semester: 3 Type: compulsory course, Lecture ECTS: 3 Exam type: written examination Design, assessment and organisational implementation of trustworthy AI systems in industrial settings; principles of Trustworthy AI, Human- Centric AI and human-AI interaction; human oversight approaches, including Human-in-the-Loop, Human-on-the-Loop and Human-in-Command; transparency, traceability and Explainable AI; bias, fairness, nondiscrimination, privacy and data governance; ethical and societal implications of industrial AI applications; risk-based regulation and compliance, particularly under the EU AI Act, including risk categories, documentation, transparency and human oversight requirements; AI governance, accountability, organisational responsibilities, risk management and AI impact assessment; roles and processes for the deployment, monitoring and continuous oversight of AI systems. Safety & Security in Industrial AI Systems Semester: 3 Type: compulsory course, Integrated course ECTS: 4 Exam type: written examination Integrated consideration of functional safety and cybersecurity in industrial AI systems; safety and security requirements for cyber-physical systems, IT/OT architectures and AI-enabled applications; hazard analysis, threat modelling and risk assessment for industrial systems incorporating AI; threats, vulnerabilities and protection mechanisms in connected production, automation and Edge AI environments; security requirements for data, models, interfaces and communication; functional safety concepts for plants, machinery and autonomous systems; AI-specific risks related to data quality, model uncertainty, bias, robustness, manipulation and limited traceability; validation, testing and safe and secure integration of AI components based on industrial use cases.

Industrial Management, Leadership & Change

	1.	2.	3.
Management 1	2	5
Management 1 Graduates possess an integrated understanding of industrial value chains, customer-centric innovation and the strategic management of Industrial AI. They can analyse material, information and data flows, identify AI opportunities and assess use cases in terms of value contri-bution, data availability, feasibility, risks, organisational implications and sustainability. They are able to apply customer-centric innovation meth-ods to develop, prototype and evaluate industrial AI and digitalisation solutions based on customer and stakeholder needs. Furthermore, they can align AI initiatives with strategic objectives and value drivers, assess organisational AI readiness and capability gaps, prioritise initiatives ac-cording to strategic fit, scalability and implementation requirements, and derive coherent AI roadmaps and project portfolios for industrial compa-nies. Industrial Value Chains & AI Opportunities Semester: 1 Type: compulsory course, Lecture ECTS: 2 Exam type: written examination Industrial value chains from sourcing and procurement through produc-tion, logistics, distribution, sales and after-sales services to the cus-tomer; material, information and data flows along industrial value chains; identification of opportunities to improve efficiency, quality, resilience and sustainability in industrial processes; AI opportunities across the value chain; assessment of industrial AI use cases in terms of value contribution, data availability, feasibility, organisational implications, risks and sustainability. Industrial AI Strategy Semester: 2 Type: compulsory course, Lecture ECTS: 2 Exam type: written examination Strategy and strategic management in industrial companies, including corporate, business-unit and functional strategies; alignment of AI initi-atives with strategic objectives, competitive positioning, value drivers and functional priorities; assessment of AI readiness and capability gaps in areas such as data, technology, processes, skills, governance, culture and organisational structures; selection and prioritisation of AI initiatives according to strategic fit, value contribution, feasibility, scalability, risks and implementation requirements; development of strategic AI roadmaps, technology choices and AI project portfolios for industrial companies. Industrial Innovation & Customer-Centric Solutions Semester: 2 Type: compulsory course, Integrated course ECTS: 3 Exam type: written examination Customer-centric innovation in industrial contexts, including types of in-novation, innovation processes and the role of customer value in inno-vation management; application of Design Thinking and related innova-tion approaches to the development of industrial AI use cases; problem exploration, customer and stakeholder analysis, problem definition, ide-ation, concept development, prototyping, testing and iteration; evalua-tion and prioritisation of solution concepts in terms of desirability, feasi-bility, economic viability and industrial impact.
Management 2			5
Management 2 Graduates can lead interdisciplinary teams and support organisational change in technological and digital transformation contexts. They are able to reflect on their own leadership behaviour, motivate and develop individuals and teams, exercise lateral leadership, communicate effec-tively with stakeholders, and address resistance, uncertainty and conflict constructively. They can analyse the economic implications of technical, digital and AI-based initiatives, assess costs, benefits, risks and scala-bility, apply selected investment appraisal methods, and develop sub-stantiated business cases. By combining leadership, change manage-ment and economic evaluation, they can support industrial technology initiatives from organisational mobilisation and stakeholder engagement to economically sound decision-making and implementation. Engineering Economics & Industrial Business Cases Semester: 3 Type: compulsory course, Integrated course ECTS: 2 Exam type: written examination Economic evaluation of technical, digital and AI-based solutions in industrial contexts; cost accounting, calculation and cost structures of industrial products, processes and projects; investment appraisal using selected methods such as payback period, net present value, return on investment and total cost of ownership; development and evaluation of business cases for AI, automation and digitalisation projects; estimation of potential benefits, implementation costs, operating expenses, risks and scaling effects; economic evaluation of make-or-buy decisions, technology alternatives and implementation options; preparation of decision-support materials for technical and management decisions; application in a business simulation involving industrial technology and investment decisions. Leadership & Change Management Semester: 3 Type: compulsory course, Integrated course ECTS: 3 Exam type: continuous assessment Leadership and change management in interdisciplinary technological and digital transformation contexts; self-leadership and reflection on personal competencies, behaviours, attitudes and development needs; development of individual competence development plans; leadership of individuals and teams, including motivation, delegation, feedback, psychological safety and a shared team vision; lateral leadership and influence without formal authority; identification of change needs and design of organisational change processes from stakeholder analysis and communication to implementation and stabilisation; selected change models and instruments; stakeholder engagement, expectation management and participatory decision-making; constructive management of resistance, uncertainty, conflicts, coalitions and crises; application to technological projects in industrial companies.

Projects & Scientific Working

	1.	2.	3.	4.
Projects 1	2	3
Projects 1 Graduates can structure, address and develop prototype solutions to practice-oriented industrial challenges in Digital Engineering and Indus-trial AI within interdisciplinary teams. They are able to analyse require-ments for basic cyber-physical systems incorporating AI, define tech-nical interfaces between engineering, sensing, actuation, software and data processing, and test data pipelines and AI-based solution ap-proaches in an application-oriented system context. Furthermore, they can assess industrial AI use cases in terms of the problem, available data, technical feasibility, potential value and opportunities for further development, apply appropriate project management methods, and clearly document, reflect on and present project results. Industrial AI Systems Lab Semester: 1 Type: compulsory course, Project ECTS: 2 Exam type: written examination Interdisciplinary development of a basic cyber-physical system incorpo-rating AI; analysis of system requirements and definition of technical in-terfaces between engineering, sensing, actuation, software and data processing; development of a data pipeline from data acquisition and preprocessing to its use in an AI model; integration and testing of hard-ware, software and AI components within a prototype system; analysis of system behaviour and evaluation of technical functionality; team-based documentation and presentation of results; Students may select a preferred topic from a range of available Industrial AI lab topics (final allocation is determined by the head of studies based on didactic and organisational requirements). Practical Project 1 Semester: 2 Type: compulsory course, Project ECTS: 3 Exam type: continuous assessment Team-based work on a practice-oriented industrial challenge; identifica-tion and structuring of an industrial AI use case; analysis of the problem, requirements, available data, technical conditions and potential value; design and prototype implementation of an AI-based system or AI-ena-bled application; application of appropriate project management and ag-ile collaboration methods; validation, evaluation and reflection of the de-veloped solution regarding technical feasibility, industrial applicability and further development potential; preparation of project documentation and presentation of results; Students may indicate preferences among a range of industry-based project assignments or submit their own topic proposals in cooperation with industry partners (final approval and allo-cation are determined by the head of studies based on didactic and or-ganisational requirements).
Projects 2			9
Projects 2 Graduates can address complex industrial problems in Digital Engineer-ing and Industrial AI with a high degree of autonomy and develop robust, application-oriented results. They are able to analyse technical, data-related, economic and organisational conditions holistically, combine appropriate methods from engineering, cyber-physical systems, Indus-trial AI, simulation, project management and economic evaluation, and derive viable solutions, implementation or decision-making concepts. Furthermore, they can validate project results, assess potential value, risks, scalability and implementation effort, and prepare professional de-cision-support materials, documentation and presentations for industrial clients. The focus is no longer on the initial application of individual meth-ods, but on the integrated use of the acquired competence profile to ad-dress extensive real-world challenges with a high level of technical depth and practical relevance. Practical Project 2 Semester: 3 Type: compulsory course, Project ECTS: 9 Exam type: continuous assessment Work on an extensive, practice-oriented and preferably industry-related challenge in Digital Engineering and Industrial AI; independent structuring of the problem from requirements analysis and concept development to prototype implementation or the development of a substantiated solution; application of key competencies from engineering, cyber-physical systems, Industrial AI, data processing, simulation, project management and economic evaluation; collaboration in interdisciplinary project teams and coordination with clients; assessment of feasibility, potential value, risks, scalability and opportunities for further development; preparation of professional project documentation and presentation of results; Students may indicate preferences among a range of industry-based project assignments or submit their own topic proposals in cooperation with industry partners (final approval and allocation are determined by the head of studies based on didactic and organisational requirements).
Master Thesis				30
Master Thesis Graduates are able to independently investigate a complex practice-ori-ented or industrial problem in Digital Engineering and Industrial AI using scientific methods. They can structure problem areas, identify, evaluate and use relevant literature, formulate appropriate research questions, and develop and implement a transparent methodological approach. Furthermore, they can derive academically sound conclusions, insights and alternative solutions, document their results in accordance with for-mal academic standards, and comply with key requirements concerning scientific practice, citation, copyright, traceability and the publication of academic work. Master Thesis Semester: 4 Type: compulsory course, Master's thesis ECTS: 27 Exam type: continuous assessment The Master Thesis generally addresses a practice-oriented industrial problem within the competence and professional fields of the Digital Engineering & Industrial AI degree programme. It involves the academically sound and application-oriented investigation of complex questions at the intersection of engineering, digitalisation, cyber-physical systems and Industrial AI. Students integrate the subject-specific, methodological and scientific competencies acquired during the programme and combine them with experience from professional or industrial practice. The Master Thesis is usually completed in connection with a challenge or project provided by an industrial company and may build on preparatory work, research questions or results from Practical Project 2 in the third semester. Master's exam Semester: 4 Type: compulsory course, Final Exam ECTS: 2 Exam type: oral or written examination Thesis Preparation Seminar Semester: 4 Type: compulsory course, Seminar ECTS: 1 Exam type: continuous assessment Ongoing subject-specific and academic support during the preparation of the Master Thesis; discussion, analysis and evaluation of research questions, methodological approaches, interim results and key findings; support in structuring and academically developing the thesis; reflection on the practical relevance, academic quality and traceability of results; preparation for the final presentation and Master Final Exam.

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Email sekretariat.deia@fh-wels.at

Telephone +43 5 0804 43053

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Master's degree programs at the Wels campus

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