Computational Life Sciences (B. Sc.)

This module introduces the fundamentals of software development and programming in Java. Topics include algorithmic thinking, core language elements, control structures, object-oriented concepts, exception handling, arrays, and UML class diagrams. In the lab sessions, students deepen their knowledge through programming exercises using professional tools such as Eclipse, JUnit, and debugging environments.

This module introduces fundamental concepts of computer science. Topics include algorithms and their representations, complexity and computability theory, number and information representation, and logic. Students also explore the structure of modern computer systems, from the von Neumann architecture to advanced concepts such as caching, multi-core systems, pipelining, superscalar processing, and GPU computing.

This module provides the mathematical foundations for computer science and Computational Life Sciences. Topics include propositional and predicate logic, proof techniques, modular arithmetic, complex numbers, limits and continuity. Students also study differential and integral calculus, including Taylor polynomials, Taylor series, and infinite series, with a focus on their fundamental applications.

This module provides an introduction to genetics, microbiology, and immunology. Topics include the structure and function of genetic elements, DNA replication and repair, gene expression, and the genetics and pathogenicity of bacteria and viruses. Students also learn about diagnostic methods, genomics and genetic engineering, as well as key mechanisms of immune defense and vaccine-based prevention through selected practical examples.

This module introduces the fundamentals of academic research, information sources, and library use. Students work in small teams on subject-specific tasks, develop initial projects, and present their results. The course also covers teamwork, learning strategies, and time management. An excursion and industry talks provide insights into practical applications of Computational Life Sciences.

This module introduces the foundations of health, prevention, and public health. Topics include determinants of health and disease, epidemiological methods, evidence-based medicine, and health data and communication. Students explore national and international health systems, health promotion and prevention strategies, health inequalities, and the role of innovation and technology in addressing global health challenges.

This module introduces the fundamentals of software engineering across the entire development lifecycle. Topics include requirements analysis with stakeholders, use cases and UML modeling, software architecture and design concepts, as well as implementation principles and code quality. Students also learn software testing methods, including black-box, white-box, and dynamic testing techniques.

This module introduces advanced foundations of computer science. Topics include data structures such as arrays, trees, graphs, and hashing, as well as algorithmic approaches including greedy methods and dynamic programming. The course also covers operating systems, processes, memory and file management, and the fundamentals of modern communication networks, protocols, and secure data transmission.

This module provides advanced mathematical foundations for computer science and Computational Life Sciences. Topics include linear algebra with vector spaces, matrices, eigenvalues, and linear transformations, as well as multivariable differential and integral calculus. Students also study functions of several variables, optimization problems, and the fundamentals of ordinary differential equations.

This module introduces the fundamentals of biomedical engineering and medical device regulation. Topics include the classification, safety, and hygiene of medical devices, as well as diagnostic, imaging, and therapeutic technologies. Students explore systems such as ECG, EEG, ultrasound, defibrillators, and pacemakers. The course also covers monitoring techniques and selected biomedical engineering research applications.

This module introduces the fundamentals of human anatomy and physiology. Topics include cell and tissue structures, membrane physiology, substance transport, and medical terminology. Students explore the structure and function of major organ systems, including the cardiovascular, nervous, and digestive systems. Selected pathologies and connections to biomedical informatics complement the course content.

This module expands programming skills through key concepts of software development. Topics include algorithms, data types and data structures, control structures, functions, modules, and object-oriented programming with classes and objects. Advanced topics such as exceptions and event-driven programming are also covered. In the lab, students apply and deepen their knowledge using professional development tools.

This module introduces the foundations and methods of artificial intelligence with a focus on machine learning. Topics include classification, regression, clustering techniques, neural networks, feature selection, and model evaluation and optimization. Through biomedical use cases, students learn to work with real-world data and address challenges such as bias, data privacy, and incomplete datasets.

This module introduces the fundamentals of database systems and data management. Topics include database architectures, data modeling with the Entity-Relationship model, relational databases, and SQL. Students also explore transaction management, security aspects, and NoSQL databases. In the lab sessions, they deepen their knowledge through database design, schema implementation, querying, and optimization using professional development tools.

This module introduces the fundamentals of biostatistics and data analysis. Topics include descriptive and inferential statistics, probability theory, regression and correlation analysis, statistical testing, and confidence intervals. Students learn methods for analyzing and visualizing data, including time series, histograms, box plots, clustering techniques, and the visualization of high-dimensional datasets.

This module introduces the fundamentals of business administration and entrepreneurship. Topics include organization, marketing, finance, accounting, investment analysis, innovation management, and business model development. Students explore entrepreneurship and intrapreneurship concepts and apply them in a startup project by developing product ideas, business models, and marketing strategies.

This module introduces the methods of project, risk, and quality management. Students learn how to plan, manage, and monitor projects, covering goal definition, stakeholder and risk analysis, resource planning, and change management. The course also covers agile approaches such as Scrum, as well as the fundamentals and regulatory framework of quality management, particularly in healthcare settings.

This module introduces the fundamental methods and application areas of bioinformatics. Topics include molecular biology and genetics, cellular processes, evolution, and high-throughput technologies for DNA and RNA analysis. Students learn to work with biological databases and ontologies as well as pairwise and multiple sequence alignment methods for the analysis of biological sequence data.

This module introduces the foundations of medical informatics and digital healthcare systems. Topics include communication standards and interoperability solutions such as HL7, FHIR, and DICOM, as well as concepts for integrating distributed healthcare environments. Students explore key applications including electronic health records, e-prescriptions, and digital hospital processes.

This module introduces the fundamentals of health economics and healthcare systems. Topics include healthcare markets, health policy, key stakeholders, and economic evaluation methods. Students learn to assess indicators of the German healthcare system in an international context and to plan and conduct health economic analyses and evaluations, particularly in relation to digital transformation in healthcare.

This module introduces the fundamentals of biochemistry and pharmacology. Topics include the structure and metabolism of proteins, lipids, and carbohydrates, enzymes, and key cellular energy pathways. Students learn about drug mechanisms, pharmacokinetics, pharmacogenetics, major drug classes, drug interactions, and modern approaches to drug development, including biologics and biosimilars.

This module introduces medical image processing from image acquisition to AI-based analysis. Topics include preprocessing, digitization, feature extraction, segmentation, morphology, and classification. Major imaging modalities such as X-ray, CT, MRI, ultrasound, and microscopy are covered, alongside modern deep-learning approaches for image analysis, segmentation, object detection, and instance segmentation.

This practice-oriented course develops communication and interpersonal skills for professional settings. Students learn the fundamentals of effective communication, including verbal and non-verbal signals, conversation techniques, and conflict prevention and resolution. Through realistic case studies, role-playing exercises, and feedback sessions, they reflect on and strengthen their communication and presentation skills.

The internship enables students to apply the knowledge and skills acquired during their studies in a professional environment. Students select a suitable company in Germany or abroad and independently work on defined tasks using scientific methods. They prepare a work plan with specific work packages and document their activities, results, and reflections in a comprehensive internship report.

This accompanying practice course supports the reflection and evaluation of experiences gained during the internship semester. Students present their activities and findings in short presentations and discuss them with their peers. The course links practical experience with theoretical knowledge while fostering professional, communication, and social skills through discussion, collaboration, and group-based learning processes.

This module introduces the fundamentals of IT security in healthcare. Topics include regulatory requirements, threats, and protection mechanisms for IT systems, as well as cryptography, network security, and access control. Students learn security principles, risk analysis, security architectures, and security management. The course also covers the secure operation of healthcare applications and relevant certifications in the healthcare sector.

This module introduces methods for modeling and simulating biomedical systems. Students learn the model development cycle from hypothesis generation to validation and apply models to problems in demography, ecology, epidemiology, and population genetics. Key topics include data-driven and knowledge-based modeling, gene regulatory networks, and the representation of dynamic processes using Boolean networks and difference equations.

This module expands students’ knowledge of bioinformatics methods and applications. Topics include omics technologies in genomics, transcriptomics, proteomics, metabolomics, and epigenetics, as well as their analysis. Further areas of study include phylogenetic methods, protein structure prediction, and approaches to sequence analysis and gene prediction, including pattern recognition, weighted matrices, Markov chains, and hidden Markov models.

This module introduces the fundamentals of digital transformation in healthcare through the modeling and implementation of digital processes. Students learn how to set up development environments, model BPMN-based workflows, and execute them using process engines. The course also covers the configuration of relevant process elements as well as methods for monitoring and analyzing running processes.

This module introduces the ethical, legal, and data protection foundations of digital healthcare and life sciences. Topics include ethical theories, human–machine interaction, algorithmic bias, transparency, and accountability of digital systems. Students also learn about legal frameworks, liability issues, data protection, copyright, and regulatory requirements, illustrated through eHealth and life science applications.