• Successfully completed bachelor’s degree (180 ECTS) with specialisation in mathematics/statistics and computer science; e.g.: Biotechnology and Instrumentation Engineering, Digital Logistics, Digital Technologies, Electrical Engineering, Computer Science, Engineering Computer Sciences, Applied Mathematics, Mechatronics, Mechatronics/Automation, Business Information Systems.
• The previous degree must have been completed with an overall grade of at least 2.5
• English language skills at level B2 must be demonstrated
• Successfully passed aptitude test

The Research Master’s programme trains you to become an applied science researcher in data science. To this end, you will reach the following study objectives:

• Mastering data mining methods and algorithms for processing, analysing and exploiting large amounts of data
• Being able to apply machine learning algorithms to the development of decision support systems based on large amounts of data
• Developing generative models for the generation of images, videos and texts
• Gaining in-depth expertise in the development of autonomous AI-based software agents
• Developing, configuring and using big data architectures for batch and stream processing
• Mastering multi-paradigm programming with Python and its relevant ML libraries
• Being able to perform independent, applied scientific work in interdisciplinary project teams, including writing research abstracts, preparing scientific publications and discussing and defending one’s own results in a plenary session
• Mastering agile project management skills for leading innovative project teams
• Being able to reflect on one’s own work, especially in light of the limits of scientific knowledge and ethical considerations

All courses are taught in English.

1st semester

• Project Phase I (12 ECTS)
• Introduction to Applied Research (6 ECTS)
• Compulsory Elective Subject Data Science (6 ECTS)
• Scientific Interchange (1 ECTS)
• Project-Specific Elective Module (5 ECTS)

2nd semester

• Project Phase II (7 ECTS)
• Agile Research Project Management (6 ECTS)
• Compulsory Elective Subject Data Science (6 ECTS)
• Compulsory Elective Subject Data Science (6 ECTS)
• Project-Specific Elective Module (5 ECTS)

3rd semester

• Project Phase III (12 ECTS)
• Social Implications of Data Science (6 ECTS)
• Compulsory Elective Subject Data Science (6 ECTS)
• Scientific Interchange (1 ECTS)
• Project-Specific Elective Module (5 ECTS)

4th semester

• Master Thesis (24 ECTS)
• Colloquium (6 ECTS)

Compulsory Elective Subjects Data Science

• Introduction to Data Science
• Big Data Architectures
• Data Mining & Machine Learning
• Artificial Intelligence
• Advanced Machine Learning
• Artificial Intelligence for Robotics

As a data science professional, you will be highly sought after by employers and able to choose one anywhere in the world! After completing your studies, you can pursue a range of different career paths, including:

• Career in applied science: Work as a research associate in applied research at universities, universities of applied sciences or public research institutes and earn your doctorate by writing an application-oriented doctoral thesis; this can also be done in cooperation with a company.
• Career in industry: As a data scientist, you can help shape the digitalisation of your company. Work in research and development and take a leading role in creating the company’s new digital service with your team.
• Start up your own company: Based on the results of the research conducted during your studies, launch your own digital company together with your fellow students with optional support from the university.

Areas of application

The digital revolution touches all areas of life and work. The possible areas of application are accordingly diverse. Through your research project, you can gain in-depth knowledge of the field during your studies, often already in collaboration with renowned companies from the strong industrial region of East Westphalia-Lippe (known as OWL in German). Possible business areas include automotive, engineering, banking and insurance, retail, IT, management and organisational consulting, market research, social media, telecommunications, online retail and network management, bio, pharmaceutical, chemical and medical industries, healthcare and logistics.

• Study a unique programme with highly effective practical learning through permanent project orientation and active advancement of the current state of research
• Collaborate in exciting research projects in interdisciplinary teams of experts
• Produce first publications and present your own research at international conferences
• Conduct research on topics that are relevant to the numerous renowned companies and “hidden champions” in the OWL region
• Prepare for (project-based) professional work under ideal conditions thanks to the programme’s project-based approach
• Profit from an optimal learning environment that offers individual supervision during the project phases and teaching in small groups of approximately 20 students in the other modules
• Profit from uniquely close supervision and support by HSBI’s professors throughout the programme
• Prepare for a subsequent doctorate in applied research in the best possible way
• Take part in lively exchange across the semesters with lecturers, fellow (international) students and peers in project colloquia, research seminars, courses and forums on the online platform ILIAS
• Start your studies flexibly in the winter or summer semester

Project proposals are published in spring and autumn. Applicants with German certificates can apply online through HSBI’s website. Applicants with certificates issued abroad should check the International Office’s website to find out which application portal they must use to apply.

As part of the application procedure, the following documents must be uploaded to the application portal: degree certificate of the university degree qualifying for the master’s degree programme and the associated documents such as transcript of records, diploma supplement, etc. and a recognised English certificate. In addition to the general documents, please submit a one-page letter of motivation in which you apply for a project from the project pool. Please also name two alternative projects with priorities 2 and 3 and elaborate your interest in these as part of your letter of motivation.

The entrance examination for the study programme takes place at the end of the semester preceding the one in which you intend to take up studies. The examination will take place online and all aids are allowed. Sources, including generative LLMs, must be specified. Direct quotations from generative LLMs are not allowed and will result in a failed examination. The aim of the entrance examination is to verify that applicants have the following competencies:

1. Ability to master a topic in a professional/scientific manner
2. Methodical approach in developing solutions; systematic way of assessing one’s own solutions
3. Ability to develop original solutions
4. Ability to structure and present a scientific topic; common thread; focusing on essential information
5. Verbal skills

You will receive the acceptance or rejection notice regarding the admission to the study programme within one week. Subsequently, a learning agreement will be made with the lecturer responsible for the project.

Dates for the application process for the summer semester 2025

• Publication of new project proposals: 6 December 2024
• Online presentation of the master’s programme: 19 December 2024, 5 p.m.–6 p.m. (CEST)
• Application for the projects by: 15 January 2025
• Entrance examination (online): 22 January 2025, 9 a.m.–11 a.m. (CEST)
• Assignment to projects by: 31 January 2025

Please find the project descriptions in the PDF that you can download on the left-hand side of this page.

• TextureSense Enhancing Object Recognition and Activity Awareness with Wearable Laser Speckle Imaging
• Interpretable and transferable AI for StarCraft2
• Cognitive Edge Computing for AI-ML-based Surface Inspection
• Decision support in production and logistics using reinforcement learning
• AI for highly flexible multi-variant mass production
• Learning Digital Twins
• Adaptive Sensor Activation with active machine learning for IoT supported quality assurance
• Deep Active Learning Intelligent Training Data Selection for deep learning models
• Efficient Execution of Object Detection Algorithm on Edge Devices
• Hardware AI Codesign for efficient execution of neural networks