The specialisation in Biomedical Engineering focuses on providing you with interdisciplinary knowledge of the interface between technology and medicine. Two main themes define the specialisation:

1. Biomedical Signals – covering the journey from physiological signal acquisition to clinical decision-making, and
2. Biomedical Structures – focusing on translating medical images into physical and numerical models.

The specialisation is comprised of 40 ECTS core courses.

Spring semester:

Biomedical Signal Analysis and Machine Learning (10 ECTS)
This course equips students with the skills to design and implement complete analysis pipelines for biomedical time series data, such as ECG and EEG. You learn to describe signals as stochastic processes and to apply standard preprocessing techniques, including filtering, artifact detection, and resampling. The course introduces classical machine learning models, such as linear models, decision trees, and support vector machines, for hypothesis testing and feature-based classification. Emphasis is placed on practical implementation using Python, enabling you to handle real-world data from wearables, fitness trackers, and clinical devices.

Clinical Imaging Technologies (5 ECTS)
This course introduces five major diagnostic imaging modalities used in clinical practice: Magnetic Resonance Imaging (MRI), X-ray/CT, Nuclear Medicine Imaging, Ultrasound, and Optical Imaging. You gain an understanding of the physical principles, instrumentation, and clinical applications of each method. Topics include MR spin dynamics, CT dose optimization, radioactive decay, Doppler ultrasound, and optical coherence tomography. The course emphasizes the ability to compare imaging techniques and assess their suitability for different clinical scenarios based on resolution, contrast, signal-to-noise ratio, and biological effects.

Telemonitoring project (5 ECTS)
In this hands-on project course, you develop a full-stack telemonitoring solution based on a clinical or home-care scenario. Using physiological signals, such as ECG data from wireless Holter monitors, students design, implement, and evaluate systems that integrate signal analysis, decision support tools, and interfaces for clinicians or users. The course includes an introduction to telemedicine concepts and emphasizes selecting relevant features or classifiers from physiological data. You work across the full pipeline, from sensor data acquisition to digital applications, gaining insight into technologies, instrumentation, and healthcare applications in both primary and secondary sectors.

Fall semester

Biomedical Physics (10 ECTS)
This course introduces the fundamental physical principles used to describe and understand biomechanical, biofluid, and electrophysical phenomena in the human body. Key topics include material deformation (stress-strain relationships, elasticity, bending, torsion), fluid dynamics (Navier-Stokes equations, laminar and turbulent flow, pulsatile flow, flow in elastic vessels), and electromagnetism (electric fields, potentials, magnetic fields, and induction). You learn to analyze the mechanical behavior of biological tissues and implants, describe different flow regimes in blood vessels, and solve basic electromagnetic problems using mathematical formalism.

Medical Image Analysis (5 ECTS)
This course provides theoretical and practical knowledge in digital image processing with a strong focus on medical imaging, especially MRI. You learn to implement image enhancement, restoration, segmentation, registration, and classification techniques. Core topics include spatial and frequency domain filtering, noise reduction, morphological operations, feature extraction, and 3D visualization. The course also introduces atlas-based methods and convolutional neural networks (CNNs) for segmentation. Emphasis is placed on developing the ability to independently program and apply complex algorithms for processing medical images.

Experimental modelling project (5 ECTS)
This course introduces students to scientific methods in an experimental context, focusing on modeling physiological systems using both numerical simulations and in vitro approaches. Core components include structural mechanics, material elasticity, segmentation of medical DICOM images, and Finite Element Analysis (FEA). You design and conduct experiments, collect and analyze data, and compare theoretical predictions with experimental results. Throughout the course, students also develop scientific communication skills through writing structured reports and presenting their findings orally.

If you have not had a formal introduction to anatomy and physiology, you are recommended to complete Health Science 1 (10 ECTS) and Health Science 2 (5 ECTS) during your Bachelor’s programme. Alternatively, you may take Health Science 2 as an elective in the Master’s programme. If you have had prior anatomy coursework, you may instead take Human Physiology as a specialisation elective on the Master’s level.

If you have not had a formal introduction to anatomy and physiology, we recommend that you complete Health Science 1 (10 ECTS) and Health Science 2 (5 ECTS) during your Bachelor’s programme. Alternatively, you may include Health Science 2 as an elective in the Master’s programme. If you have had prior anatomy coursework, instead you may take Human Physiology in Health and Disease as an elective course.

Advanced Electrophysiology (10 ECTS)
This advanced course focuses on the analysis of EEG and MEG signals, with broader relevance across electrophysiological modalities. You learn theoretical and numerical methods for modeling electromagnetic fields in biological tissue, signal preprocessing, time-frequency analysis, event-related potential processing, and statistical testing. The course includes practical work in designing and implementing an EEG experiment, source localization methods, and preparing a scientific poster for evaluation. It equips you to independently conduct and analyze electrophysiological studies using advanced signal processing techniques.

Cardiovascular Instrumentation (5 ECTS)
This course provides you with knowledge of measurement techniques for cardiovascular and respiratory physiology. Topics include transducer principles, blood pressure measurement (oscillometric, catheter-based), cardiac output estimation (Fick’s method, thermodilution), and blood flow and oxygenation measurement techniques. You gain an understanding of the physiological origins of measured signals and the engineering challenges involved in creating safe and accurate medical instrumentation for clinical use.

Electrophysiology and Instrumentation (5 ECTS)
Focusing on the measurement and instrumentation of bio-potentials, this course covers the theoretical and practical aspects of electrophysiological signal acquisition. Topics include neural, brain, muscular, and cardiac bio-potentials, electrodes and signal characteristics, instrumentation systems, noise reduction, and electrical safety. You learn to describe bio-potential origins, evaluate electrode technologies, and analyze sources of interference in clinical signal acquisition systems.

Klinisk projekt 1 – 3 (5 ECTS)
These clinical projects offer you hands-on experience through 74-hour internships in departments such as Cardiology, Cardiothoracic Surgery, Radiology, MR Imaging, Anesthesiology, Orthopedics, and Nuclear Medicine. You participate in routine clinical tasks, observe procedures, and familiarize themselves with clinical workflows, terminology, and diagnostic techniques. Alternatively, you may explore experimental research labs or technological innovation units. You must complete a logbook signed by department supervisors.

Learning outcomes include the ability to describe patient groups, treatment methods, procedures, or research facilities relevant to clinical biomedical engineering.