Master’s degree
Computer Science

There are a few additions to the facts about our Master's degree programme listed above: 

• Language of instruction: German
• Admission requirements:
  • Bachelor's degree in Computer Science, Business Informatics or equivalent 
  • If applicable, proof of a German language test (applies to foreigners with a first degree from a non-German-speaking country) 
    → see information sheets Master's degree programs 
• Duration: 
  • Full-time programme: 3 semesters
  • Part-time programme: 6 semesters
  • Degree programme with in-depth practical experience: 3 semesters

Successful graduates are offered numerous new opportunities

• Your technical knowledge qualifies you for "exciting/new" projects, such as Cloud, AI (Artificial Intelligence; also AI), IoT (Internet of Things), DevOps, Agile/Lean ...
• Your skills and personality have developed so that you are more likely to obtain a leadership role: analytical thinking, ability to deal with conflict, ability to work in a team, moderation, technical writing, conceptual work, ...
• You are able to solve complex informatics tasks in practice through independent and competent application of scientific knowledge and also to take on management tasks in a national and international environment.
• They are qualified to apply scientific methods and findings also to complex problems in informatics both in practice and in application-oriented research and development.
• A Master's degree from the TH Rosenheim entitles the holder to a doctorate.

Qualities such as the ability to work in a team, a sense of responsibility, critical faculties, communication and leadership skills as well as entrepreneurial and strategic thinking are expected of a Master's graduate.

The educational goals of the Master's programme therefore include

• the deepening and specialisation of knowledge in computer science and related fields on a scientific basis,  
• Promotion and teaching of analytical and creative skills
• (on the basis of broad subject knowledge with comprehensive methodological competence)
  • to develop problem-solving concepts as well as
  • for the new construction and further development of systems consisting of hardware and software.
• The teaching of networked and interdisciplinary application knowledge including so-called "soft skills" (incorporating economic, labour science, legal and social competences).

Your Master's programme is characterised by

• an application-oriented study concept
• practice-oriented specialisation in the chosen field of study,  
• interdisciplinarity resulting from cooperation with other faculties,
• emphasis on internationality as well as
• a project-oriented study organisation with an application-oriented scientific Master's thesis as its conclusion.

The SAP Competence Centre at Rosenheim University of Applied Sciences is networked with renowned user and consulting companies. This gives you the opportunity to make valuable contacts for your professional development while studying SAP-based Business Applications. Internships, student traineeships and entry-level positions are open to you after graduation. 

The Master of Science with a major in Applied Artificial Intelligence
prepares you perfectly for taking on high-quality professional activities that arise from the advancing digital transformation.

Career prospects with the field of study AAI

The job profiles range from the implementation-oriented Machine Learning Engineer, to the conceptually and innovatively working Data Scientist, to the strategically thinking Manager for Artificial Intelligence & Data Analytics Teams.

The recommended courses provide you with an in-depth understanding of current methods and technologies in the field of machine learning, artificial intelligence and data analytics for different application scenarios.

Individual study

You decide for yourself whether you prefer to focus on the holistic view of strategic, business value-generating options for the use of data or on the design, implementation and management of software architectures for intelligent/cognitive systems - in either case, you will train the handling of high complexity and your mental flexibility.

M2 modules to be taken: Machine Learning, Deep Learning

Seminar to be taken (M3): Either "Theoretical Computer Science" or "Special Business Administration". 

The Master of Science with specialisation in Software & Systems Engineering
prepares you perfectly for taking on responsibility and technically very demanding tasks in software and systems engineering project and product companies.

Career prospects with the SSE degree programme

The job profiles range from the development-oriented software architect and embedded systems engineer, to the project-responsible software product owner, to the product-responsible systems architect.

The recommended courses provide you with an in-depth understanding of current methods and technologies in the field of software and systems engineering, as well as skills in project management and leadership.

Individual study

You decide for yourself whether you want to take on more technically demanding tasks in software and systems engineering projects or more responsibility and strategic design influence on projects and products.

M2 modules to be taken: Software quality assurance, systems engineering

Seminar to be taken (M3): Theoretical computer science

Job description: 
Machine learning engineers are masters of software engineering in the field of data-driven systems and design architectures for harnessing data. In particular, they use the appropriate technologies and toolchains from the fields of AI, ML, big data and cloud computing to ensure the scalability of the architecture to large data volumes. The focus of their work is the complete data lifecycle, from data generation to data use. ML Engineers mainly work on the operational and architectural level and often detached from a specific industry.
Often ML Engineers also specialise in a more concrete application scenario (e.g. computer vision, ...).

Tasks:

• Designing strategies
  • for data integration within an organisation
  • for the implementation of data evaluation
  • scalable analysis systems and for system landscapes
• Supporting data-driven applications in the areas of requirements management (change control) and operation (operations)
• Implementing data-driven applications and systems by means of integrated (end-to-end) toolchains
• Productive implementation of specific ML, AI and data analytics application scenarios 

Job description:
AI & Data Analytics Managers Teams coordinate projects and organisational units in the context of data-driven applications and business models. Depending on the organisational structure of the company, they can be located either in the IT organisation or in a specialist department. The focus of their daily work is on combining knowledge from the areas of machine learning (ML), artificial intelligence (AI) and data analytics and know-how from the application domain with management and leadership skills. In this role, individuals work predominantly at the management and strategic level and often interface with other specialist areas.

Tasks:

• Performing management tasks in the implementation and operation of data-driven projects.
• Leading employees in the context of subject-relevant teams
• Designing the strategic use of data
• Acting as an interface to other business units
• Conduct effort estimation for project resources
• Evaluate the compliance of products/projects also with regard to data protection, ethics, social responsibility

Job description:

Typical roles are software architect, chief designer, technical project manager, CTO or IT consultant. A software architect

• is responsible for the quality, operability and buildability of a software system,
• has technical responsibility for employees,
• is active in the acquisition of new projects, offer calculation, technical consulting, professional personnel selection.

Tasks:

• Experienced software developer, programming at least throughput
• leads team of developers professionally/technically, provides framework, discusses  
• communicates with the customer, IT operations, IT security department, ...
• Develops/designs technical vision, evaluates new technologies, alone or in team
• Technology: cloud, IoT, IT security, AI, DevOps, ...

Industries:

• IT project houses
• IT product houses
• Product manufacturers
• Service providers
• Banks / insurance companies / tourism companies, logistics companies, trade, ... (each with in-house SW development)

Job description:

As a Software Product Owner or Software Product Manager you have

• Responsibility for (software) product and services
• Product responsibility

Tasks:

• Has an overview of the entire software product
• Requirements
• Innovation and product management
• Customer contact
• Interface to developers
• Pricing of products and services

Industries:

• IT SMEs
• Web + Enterprise
• Cloud Infrastructure + Applications
• Mobile Applications
• Production Technology + Mechanical Engineering
• Automotive
• Aerospace
• Mobile Communications
• Consumer Electronics
• Medical technology
• Environmental and energy technology
• Railway and defence technology
• Clothing (wearables, fintech)
• IoT, IIoT, Industry 4.0

Job description:

A Systems Architect has

• Responsibility for complete system
• Project responsibility

Tasks:

• Has an overview of the entire product, including hardware and software.
• software, hardware, cloud
• Requirements
• Software and product (system) quality
• System architecture

Industries:

• Production engineering
• Automotive
• Aerospace
• Mobile communications
• Consumer Electronics
• Medical technology
• Environmental and energy technology
• Railway and defence technology
• Mechanical engineering
• Clothing (wearables, fintech)
• IoT, IIoT, Industry 4.0

Job description:

The Embedded Systems Engineer has responsibility for embedded software in the role of Developer, Architect or Technical/Embedded Software Lead.

Tasks:

• Software for device or physical process
• Interface to electrical and mechanical engineering
• Development of embedded and real-time systems software
• Safety-critical systems: Functional Safety
• Software (device) quality + usability

Industries:

• Production engineering
• Automotive
• Aerospace
• Mobile communication
• Consumer Electronics
• Medical technology
• Environmental and energy technology
• Railway and defence technology
• Mechanical engineering
• Clothing (wearables, fintech)
• IoT, IIoT, Industry 4.0