Artificial Intelligence for Connected Industries - School of Advanced Professional Studies

Artificial Intelligence for Connected Industries

The AI4CI Master is a European master program which covers:

fundamentals of artificial intelligence and machine learning applied to networked systems
advanced automation systems for industrial networks and robotics
advanced network architectures, IoT and computer systems

The lecturers include world-class academics from our European partners and industry experts.

The master program is open in the following European Universities with different study modalities. It is supported by the Erasmus+ framework.

Cnam, Paris (France)
full time Master – applications via Campus France
Cnam Grand-Est, Mulhouse (France)
in apprenticeship – application via the TMM platform
Avignon Université, Avignon (France)
full time Master – opening planned for winter semester 2025/26
Universitat Politècnica de Catalunya, Barcelona (Spain)
full time Master – opening planned for winter semester 2025/26
Universitatea Babeș-Bolyai, Cluj-Napoca (Romania)
full time Master – opening planned for semester semester 2025/26
National Technical University of Ukraine, Kyiv (Ukraine)
full time Master – opening planned for semester semester 2025/26
Ulm University, Ulm (Germany)
academic continuing education Master (part time) – opening planned for semester semester 2025/26
Microcredentials already available.

Master of Science

(90 ECTS)

Informatik und Mathematik, Ingenieurwissenschaften

Berufsbegleitendes Weiterbildungsstudium

Veranstaltungsbeginn: 01.10.2025

Anmeldefrist: 15.07.2025

Anbieter: Universität Ulm

Sprache: englisch

Inhalte in Artificial Intelligence for Connected Industries

Module bzw. Zertifikatskurse

Business Process Management

(6 LP/ECTS) — Blended Learning mit Präsenzphasen, 1500 €

Data & Process Mining

(6 LP/ECTS) — Blended Learning mit Präsenzphasen, 1500 €

Design Methodology of Embedded Systems

(6 LP/ECTS) — Blended Learning mit Präsenzphasen, 1170 €

Einführung in die Programmierung mit Python für Data Science

(3 LP/ECTS) — Blended Learning mit Präsenzphasen, 750 €

Grundlagen des maschinellen Lernens mit Python

(3 LP/ECTS) — Blended Learning mit Präsenzphasen, 750 €

Management Aspects of Systems Engineering I

(6 LP/ECTS) — Blended Learning mit Präsenzphasen, 1170 €

Management Aspects of Systems Engineering II

(6 LP/ECTS) — Blended Learning mit Präsenzphasen, 1170 €

Modellbildung und Identifikation

(6 LP/ECTS) — Blended Learning mit Präsenzphasen, 1170 €

Pattern Recognition and Deep Learning

(6 LP/ECTS) — Blended Learning mit Präsenzphasen, 1170 €

Project Management – Processes, Activities and Practices

(6 LP/ECTS) — Blended Learning mit Präsenzphasen, 1170 €

Signals and Systems

(6 LP/ECTS) — Blended Learning mit Präsenzphasen, 1170 €

Systemtheorie und Regelungstechnik

(6 LP/ECTS) — Blended Learning mit Präsenzphasen, 1170 €

Wireless Sensor Networks

(6 LP/ECTS) — Blended Learning mit Präsenzphasen, 1170 €

Gebühren von Zertifikatskursen und CAS-/DAS-Angeboten nach Immatrikulation, Entgelte ohne Immatrikulation können höher ausfallen.

Module plan

A Compulsory Modules

Artificial Intelligence and Machine Learning (AIML) for Connected Systems (6 CP)
Master thesis (30 CP)

B Compulsory Elective Modules

B1 Systems and Networks
- Parallel and Distributed Systems (6 CP)
- Wireless and Mobile Networks (6 CP)
- Wireless Sensor Networks (6 CP)
- AI in Networking and Security (6 CP)
- Network Virtualization and Automation (6 CP)
- Design Methodology of Embedded Systems
B2 Automatics and Robots
- Signals and Systems (6 CP)
- Control System Theory and Engineering (6 CP)
- Modeling and Identification of Dynamic Systems (6 CP)
- Robot Operating Systems (3 CP)
- Learning Robots (3 CP)
- Robot Predictive Maintenance (3 CP)
B3 Processes and Data Management
- Business Process Modeling (6 CP)
- Data Management and Digital Transformation in Industrial Process Automation (3 CP)
- Project Management – Processes, Activities and Practices (6 CP)
- Data Mining and Knowledge Discovery (6 CP)
- Advanced Process Mining (6 CP)
B4 Free Elective Modules
- Programming
  - MATLAB/Mathematica Programming-LAB (6 CP)
  - Advanced Python programming (3 CP)
  - Machine Learning with Python (3 CP)
  - Refresh in C & Bash Programming (3 CP)
  - Programming and Communication of a Robotic Arm (3 CP)
- Mathematics and Algorithmics
  - Operations Research (4 CP)
  - Algorithm Engineering (6 CP)
- Management
  - Ethics and Sovereignity of Digital Infrastructures (3 CP)
  - Management Aspects of Systems Engineering I (6 CP)
  - Management Aspects of Systems Engineering II (6 CP)
- Networks
  - Network architecture (6 CP)
  - Peer-to-Peer Systems and Blockchain (5 CP)
  - Networks – Compelements and Applications (6 CP)
  - Integration of Virtual and Augmented Reality Technologies in Connected Industries (3 CP)
  - Industrial Internet of Things (6 CP)
  - Datacenter Design and Operations (4 CP)
  - Sustainable IoT Technologies (3 CP)
  - Complex Networks: Data Analysis and Network Science (4 CP)
- Artificial Intelligence and Machine Learning
  - Pattern Recognition and Deep Learning (6 CP)
  - Federated and Distributed Learning (5 CP)

Fragen zum Angebot? Schreiben Sie uns!

Ansprechperson

Rebecca Schöninger

Studiengangkoordinatorin Instruktionsdesign

rebecca.schoeninger@uni-ulm.de

Steffen Moser

Studiengangkoordinator Sensorsystemtechnik

steffen.moser@uni-ulm.de

Wissenschaftliche Leitung

Prof. Dr. Birte Glimm

Institute of Artificial Intelligence, Ulm University

Zielgruppe

The part-time master’s program Artificial Intelligence for Connected Industries (AI4CI) is designed for professionals in technical, scientific, and engineering fields who wish to expand their knowledge of artificial intelligence (AI) and specialize in its application to connected industrial environments. It is particularly suited for professionals working in sectors such as mechanical engineering, robotics, information technology, electronics, automotive engineering, or automation technology who want to acquire hands-on expertise in AI and connected technologies. Both recent graduates and professionals with extensive work experience are welcome.

The part-time structure of the program makes it easier for participants to balance their studies with their professional careers, thereby also accommodating students with family or other commitments.

Lernsetting

For the self-study component of the program, video lectures and/or detailed scripts with comprehensive information about the course content are provided. These videos and scripts have been specifically developed for working professionals, following the didactic concept of Ulm University. Each microcredential/course within the program includes exercises, case studies, examples, and/or quizzes.

Your lecturer and/or mentor will offer a kick-off meeting (online, at Ulm University, or in a hybrid format), a final meeting (online, at Ulm University, or in a hybrid format), and, depending on the microcredential/course you are taking, online seminars at regular intervals. These meetings will help you navigate the exercises and engage with the learning topics. Additionally, an online forum will be available for interaction with other students, along with a video conference room for study group meetings.

Voraussetzungen

Admission to the Master’s program in Sensor Systems Technology requires an initial university degree with a scope of 180 ECTS credits (European Credit Transfer System) and at least two years of relevant professional experience.

If the initial university degree has a scope of 210 ECTS credits, one year of professional experience is sufficient.

Verantwortliche Durchführung

Universität Ulm

Die School of Advanced Professional Studies (SAPS) ist Ihr Ansprechpartner für Ihre berufsbegleitende wissenschaftliche Weiterbildung an der Universität Ulm. Wir bieten berufsbegleitende Master-Studiengänge als Weiterqualifizierung und einzelne Module der Master-Studiengänge als Zertifikatskurse für eine Vertiefung in einem Fachbereich an.