PROGRAM:

M.Eng. in Data Science & Artificial Intelligence

MODE:
Full-time, On-site
LEVEL:
Master of Engineering
LANGUAGE :
English
DURATION :
4 Semesters
LOCATION:
Munich, Siemens Neuperlach Campus
CREDITS :
120 ECTS
PRICE:
4.800 € / Semester
START DATE:
October 2026
SCHOLARSHIPS:
Up to 80% for high-performing students.

The Master’s program in Data Science and Artificial Intelligence (AI) at MUDT is designed for ambitious students seeking to drive innovation in AI-powered systems. This interdisciplinary program offers a robust combination of advanced theory and applied skills in machine learning, data engineering, ethical AI, and emerging fields such as large language models (LLMs), computer vision, and generative AI.

Students will engage in real-world projects and thesis work in close collaboration with academia or industry, preparing them for cutting-edge roles in tech, business, and research.

The Master of Engineering in Data Science & Artificial Intelligence is planned to launch in October 2026 and is currently undergoing the accreditation process.

Specialisations

As you advance through the program, you can tailor your studies to your interests and career goals. With eight elective modules and the option to choose courses across multiple engineering programs, you can build a personalised focus area.
These specialisations help you deepen your expertise—whether in machine learning, cloud systems, robotics, IoT, industrial AI, or business analytics—and prepare you for the roles most in demand in today’s technology landscape.

Why Study This Program at MUDT?

Industry-Driven Curriculum: Built in collaboration with AI experts and industry leaders.

Specialization in Emerging Tech: Deep dive into LLMs, responsible AI, causal inference, and more

Location Advantage: Study in Munich, a leading European tech hub.

Hands-On Projects: Collaborate with companies on real data and AI challenges.

Entrepreneurship Focus: Develop business-ready AI products with startup and innovation modules.

Study Program: M.Eng. in Data Science & Artificial Intelligence

This Master’s program is structured around advanced core modules, specialised electives, personal development courses, an industry internship, and a research-based Master thesis. The curriculum combines deep technical learning with practical projects, giving students the skills to build and deploy modern AI systems in real engineering environments.

Summary of Credits

Semester 1 :
Foundations & Core AI Systems
30 ECTS

Core Modules:

Build a strong foundation in mathematics, programming, and AI systems:

  1. Mathematics Foundations for Data Science
  2. Python Foundations for Data Science & AI
  3. AI Essentials: From Linear Models to Neural Networks
  4. Foundations of Deep Learning & Generative Models
  5. Trustworthy AI: Ethics, Alignment & Governance
  6. Personal Development: Teaming Up
  7. Career Planning

You will develop both technical depth and team collaboration skills from day one.

Semester 2 :
Advanced AI & Specialization
30 ECTS

Core Modules:

Dive deeper into machine learning and modern AI systems:

  1. Statistical Learning & Predictive Modeling
  2. Large Language Models: Architecture & Applications
  3. Foreign Language (German or IT English)

Elective: Personalize your studies

  1. Elective 1
  2. Elective 2
  3. Elective 3

Specialize in areas like LLMs, cloud AI, cybersecurity, or applied AI in industry.

Semester 3 :
Industry Experience
30 ECTS

Industry Experience:

  1. Internship

Gain hands-on experience in companies, startups, or research institutions.

Apply your knowledge in real-world environments and build your professional network.

Semester 4 :
Research & Thesis
30 ECTS

Master Thesis:

  1. Master Thesis
  2. Business Ethics & Entrepreneurship

Advanced Electives (choose 2):

  1. Elective 4
  2. Elective 5

Work on a real-world research or industry project and graduate with a strong portfolio.

Elective Courses – Personalize Your Study Journey

Students select five electives across Semesters 2, ad 4

Customize your Master’s journey by choosing from a wide range of cutting-edge electives. The program allows you to build your own specialization based on your interests and career goals.

  • Architectures of Intelligent Agents

    Design and build autonomous systems capable of reasoning, planning, and decision-making.

  • Advanced Reasoning (RAG & Knowledge Graphs)

    Develop intelligent systems using retrieval-augmented generation and structured knowledge.

  • Generative AI for Creativity & Innovation

    DApply AI to create visual, audio, and textual content and explore its role in digital creativity.

  • Advanced Cloud Architectures for AII

    Design scalable, cloud-native AI systems using modern infrastructure and DevOps principles.

  • MLOps & AgentOps: Deploying AI Systems

    Learn how to deploy, monitor, and scale AI models and intelligent agents in production.

  • AI for Business & Industry

    Apply AI solutions to real-world challenges across industries like finance, healthcare, and manufacturing.

  • AI in Simulation & Digital Twins

    Create virtual environments and digital replicas of real-world systems for training AI models.

  • Cyber Security of AI Systems

    Protect AI systems against attacks, ensure robustness, and design secure AI architectures.

  • Data Engineering & Big Data Systems

    Build scalable data pipelines and work with distributed systems like Spark and modern data platforms.

  • Autonomous Software Development

    Use AI to automate coding, testing, debugging, and software development processes.

Flexibility

  • Choose 3 electives in Semester 2
  • Choose 2 advanced electives in Semester 4
  • Option to select electives from other programs or across semesters (where applicable)

This flexible structure allows you to tailor your expertise—from deep technical AI to applied industry solutions.

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Who Should Apply?

This program is ideal for students who:

  • have a Bachelor’s degree (Especially Engineering, AI, Data Science, Business, etc.)
  • want to build and deploy advanced AI systems (LLMs, agents, MLOps, cloud AI),
  • enjoy solving complex problems with cutting-edge technologies,
  • aim for careers like AI Engineer, ML Engineer, AI Architect, or Autonomous Systems Developer,
  • are curious about ethical, secure, and responsible AI,
  • and prefer hands-on, project-based learning connected to industry.

Admission Requirements

Applicants for the Master of Engineering (M.Eng.) in Data Science &
Artificial Intelligence must meet the following criteria:

  1. A recognized Bachelor’s degree (minimum 180 ECTS) in any discipline
  2. Proficiency in at least one modern programming language (preferably Python or R), Ability to write, debug, and document code for data analysis and algorithmic tasks, Familiarity with common DS/AI tools (e.g., TensorFlow, PyTorch, Scikit-learn, or similar)
  3. Foundational knowledge of mathematics and statistics (e.g. linear algebra, calculus, probability)
  4. Basic knowledge of computer systems, operating systems, and network technologies, Awareness of software engineering principles and version control (e.g., Git)
  5. English proficiency at CEFR B2 level or higher (e.g. IELTS 6.0 or equivalent)
  6. Strong motivation and interest in data science, AI, and applied problem-solving

 

The standard program consists of 120 ECTS, including a mandatory internship semester. Applicants with relevant prior internship or professional experience may qualify for a shortened 90-ECTS track.

If required, applicants may be asked to complete bridging courses or a qualifying assessment before enrollment.

Program Structure & Credit Option

The standard program consists of 120 ECTS and includes a mandatory internship semester. This track is completed in 4 semesters.

Qualification Goals

1

Professional Competencies

Graduates are able to:

- Design and implement advanced AI and machine learning systems

- Work with large-scale data, predictive models, and modern AI architectures

- Develop and apply Large Language Models (LLMs) and generative AI

- Build scalable, production-ready AI solutions using cloud and MLOps

- Evaluate AI systems in terms of performance, robustness, and limitations

- Apply AI methods across industry, business, and interdisciplinary domains

2

Responsible & Ethical AI

Graduates understand how to:

- Apply principles of fairness, transparency, and accountability

- Identify and mitigate bias, privacy risks, and security challenges

- Align AI systems with regulatory and societal requirements

- Design and evaluate trustworthy AI solutions

3

Methodological & Analytical Skills

Students develop the ability to:

- Apply scientific methods to complex real-world problems

- Design experiments and interpret data critically

- Evaluate models under uncertainty and real-world constraints

- Think in a structured, analytical, and solution-oriented way

4

Personal & Social Competencies

The program strengthens:

- Teamwork and collaboration in interdisciplinary environments

- Communication skills for technical and non-technical audiences

- Self-organization and independent working abilities

- Critical thinking and adaptability in fast-changing fields

5

Career Readiness

Graduates are prepared to:

- Take on technical and leadership roles in AI-driven organizations

- Work in international and multicultural environments

- Contribute to innovation and digital transformation

Academic Qualification

The program also enables graduates to:

- Conduct independent scientific research in AI and Data Science

- Pursue PhD studies or academic careers

- Complete and defend complex projects such as the Master Thesis with academic rigor

Career Opportunities, Salaries & Job Growth in Germany

Graduates of the M.Eng. in Data Science & AI are highly sought after across Germany’s technology, engineering, and industrial sectors. Typical career paths include AI Engineer, Machine Learning Engineer, AI Architect, Cloud & MLOps Engineer, Autonomous Systems Developer, and Data Scientist.

Germany offers one of Europe’s strongest job markets for AI talent, with starting salaries typically between €55,000–€75,000, rising to €90,000–€120,000+ with experience and specialization. Demand continues to grow due to rapid digital transformation, Industry 4.0, automation, and the adoption of AI in manufacturing, healthcare, finance, logistics, and consulting.

The demand for AI talent in Germany is expected to grow by over 25% annually, driven by digital transformation, Industry 4.0, and EU-backed AI investments.

{ By joining this Master of Engineering in Data Science & AI, you gain the expertise, industry experience, and technological insight needed to lead innovative AI projects, shape intelligent systems, and build a successful career in one of Germany’s fastest-growing and most impactful fields. }

Faculty Expertise:

Meet our esteemed faculty members who bring a wealth of experience and expertise in cyber security, ethical hacking, and threat intelligence. Learn from industry practitioners dedicated to shaping the next generation of cyber security professionals.
BIO

Ewa Currie, M.A.

BIO

Khaleeq Aziz

BIO

Dr. Arash Habibi Lashkari

BIO

Yılmaz Çankaya

BIO

Dr. Francisco Manuel Rangel Pardo

BIO

Dr. Jose Angel Gonzalez Barba

BIO

Dr. Marc Franco Salvador

BIO

Prof. dr. Jack Mochyla

BIO

Dr. Agnieszka Dziedzic

BIO

Prof. Dr. Pawel Gburzynski

How to apply

1

Submit Your Application:

Create an account, select your study program, and upload the required documents. We’ll confirm once everything is submitted.
2

Pass the Interview:

If your documents are approved, you'll be invited to an online interview. Succeed, and you'll receive a conditional acceptance.
3

Sign and Pay:

Return the signed study contract and complete the enrollment fee and deposit to secure your spot

Receive Your Admission Letter
Congratulations, you’re in!

Master of Engineering in Data Science & Artificial Intelligence

Apply now

FAQ

This full-time, on-campus Master of Engineering (M.Eng.) programme at Munich University of Digital Technologies & Applied Sciences (MUDT) prepares you to design, build, and deploy intelligent data-driven systems. You’ll combine advanced AI theory with hands-on projects, ethical reflection, and real industry challenges.