PROGRAM:

M.Eng. in Data Science & Artificial Intelligence

MODE:
Full-time, On-site
LEVEL:
Master of Engineering
LANGUAGE :
English
DURATION :
3 or 4 Semesters
LOCATION:
Munich, Siemens Neuperlach Campus
CREDITS :
90 or 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:

1.Foundations of Deep Learning & Generative Models – 5 ECTS

2.Large Language Models: Architecture & Applications – 5 ECTS

3.Architectures of Intelligent Agents – 5 ECTS

4.MLOps & AgentOps: Deploying AI Systems – 5 ECTS

5.Trustworthy AI: Alignment, Ethics & Governance – 5 ECTS

Personal Development:
6. Personal Development: Intercultural Communication – 5 ECTS
7. Foreign Language (German or other) – 5 ECTS

Semester 2 :
Advanced Architectures & Multi-Agent Systems
30 ECTS

Core Modules:

1.Advanced Reasoning: RAG & Knowledge Graphs – 5 ECTS

2.Multi-Agent & Collaborative AI Systems – 5 ECTS

Elective (choose 1): (6 ECTS)
3. Elective I – 6 ECTS

4. Elective II – 6 ECTS

Personal Development:
5. Personal Development: Teaming Up – 4 ECTS (from PD module group)

6. Career Planning

Semester 3 :
Practical Experience & Specialization
30 ECTS

Industry Experience:

1.Mandatory Internship

Semester 4 :
Research & Thesis
30 ECTS

Master Thesis & Research:

1.Master Thesis

2. Master Thesis Seminar / Defense Preparation

Elective (choose 1):

3. Elective  III – 6 ECTS

4. Elective IV – 6 ECTS

Personal Development:
5. Career Planning II (Final Module) – 4 ECTS

Elective Courses – Personalize Your Study Journey

Students select four electives across Semesters 2, 3, and 4

  • AI in Simulation & Digital Twins (6 ECTS)

    Develops skills for simulation environments, digital twins, robotics training, sim-to-real transfer, and domain randomization using tools like NVIDIA Isaac Sim and Gazebo.

  • Autonomous Software Development (6 ECTS)

    Focuses on AI-assisted software engineering, code-specialized LLMs, automated testing, and debugging using next-generation AI development tools.

  • Advanced Cloud Architectures for AI (6 ECTS)

    Teaches scalable cloud-native AI deployment including Kubernetes, distributed systems, and high-performance compute environments.

  • Generative AI for Creativity & Innovation (6 ECTS)

    Explores advanced generative models for automation, media creation, innovation pipelines, and enterprise workflows.

  • Robotics & Embodied AI (6 ECTS)

    Applies AI models to robotic systems, control algorithms, and embodied intelligence.

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

  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 Options

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

Applicants with relevant prior experience may qualify for an accelerated 90-ECTS track, allowing completion of the program in 3 semesters.

Eligibility for the 90-ECTS Track

Applicants must provide documented proof of relevant experience, such as:

  • – A full-time internship of at least one semester completed during Bachelor’s studies in a relevant field, or

  • – A minimum of 6 months of relevant professional work experience

👉 Important:
Applicants applying for the 90-ECTS track are required to upload internship and/or work experience confirmation letters during the application process. Documents will be reviewed as part of the admission assessment.

Applicants without relevant internship or work experience, or whose documentation does not meet the program’s standards, will be enrolled in the standard 120-ECTS track (4 semesters).

Qualification Goals

1

Advanced Scientific & Technical Expertise

Graduates gain deep knowledge of modern AI systems, including deep learning, LLMs, intelligent agents, multi-agent architectures, RAG systems, cloud-native AI, and trustworthy AI. They can design, implement, and evaluate complex AI solutions at a professional engineering level.

2

Application of AI in New & Complex Contexts:

Graduates can apply advanced AI methods in unfamiliar, multidisciplinary environments such as industry, business, robotics, simulation, and digital twins. They can integrate diverse technologies and manage complexity.

3

Responsible & Ethical AI Competence:

Graduates understand the societal, ethical, and regulatory implications of AI. They can ensure fairness, transparency, privacy, security, and alignment when developing AI systems and comply with frameworks like the EU AI Act.

4

Communication & Leadership Skills:

Graduates can clearly communicate technical results to expert and non-expert audiences, lead technical projects, work in diverse teams, and take responsibility in collaborative and interdisciplinary settings.

5

Self-Directed Professional Growth:

Graduates are prepared for independent learning, research, and innovation. They can manage their own development, pursue advanced specialization, or continue toward a PhD or research career.

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. }

Industry Cooperation

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!