Our academic year
Mandatory core courses (total 15 ECTS)
Data Science (first trimester)
This course introduces fundamental concepts and mathematical tools in data science, with the aim of preparing students for data processing in computational biomedicine. Specifically, the following topics will be covered: 1) Hypothesis testing; 2) Dimensionality reduction, using machine learning algorithms such as manifold learning, unsupervised learning and principal components analysis; 3) Classification, supervised learning and decision making processes; 4) Applications in Big Data, involving methods such as deep learning. See detailed study plan.
Research Methodology (first trimester)
This course covers the major considerations and tasks involved in conducting scientific research. It reviews the graduate studies research context, skills and methodologies. It furthermore introduces the essential aspects in research proposal writing and research reporting. See detailed study plan.
Research and Science Seminars (first, second, third trimester, 2+2+1 ECTS)
Subject of this course are regular organized interactions between the master students and members of their research group. Apart from the supervisor these will be doctoral students, postdoctoral researchers and other senior professors. These interactions include the joined discussion of ongoing scientific projects of different group members as well as the discussion and analysis of scientific articles by others. Master students will make regular presentations on their progress, summarize scientific articles in the reading groups, etc.
Elective courses (total 20 ECTS)
Students can potentially take external elective courses offered by others MSc and BSc degrees of our Departments. Important: Since these courses are from other degrees it cannot be guaranteed that places are available. Furthermore, it cannot be guaranteed that the schedules of these courses are compatible with those of our courses. Availability and schedule compatibility must be checked before enrollment. See full list of external courses.
Quantitative Cell Biology (first trimester)
Current genomic technologies generate large amounts of data that quantify the expression of thousands of genes in single cells, spearheading the entry of the field of cell biology into the era of big data. This information is crucial to understand how cells coordinate their behavior to form multicellular organisms (such as human beings) and regulate their functioning in both health and disease. This course provides an overview of the statistical and modeling methods that are being developed to extract and interpret information from genome-wide single-cell data. The methods include principal component and cluster analyses, machine-learning approaches to detect patterns within the data, and Bayesian inference for model selection, among others. The course will have a strong practical component, making use of a variety of genomic and proteomic databases. See detailed study plan.
Computational Bioelectromagnetism (second trimester)
Description and use of computational tools for solving bioelectricity and bioelectromagnetism problems. Problems tackled by biomedical engineers and researchers within these areas will be presented and related practical exercises will be performed using available software platforms. See detailed study plan.
Advanced Biosignal Analysis (second trimester)
Subject of this course is nonlinear analysis of biomedical signals. In the theory sessions we will learn about different advanced signal analysis approaches such as nonlinear predictability measures and Monte Carlo simulations designed for null hypothesis testing. As an example of biomedical signals, we will study why and how the electroencephalogram (EEG) is recorded from inside the brain of epilepsy patients. This will be complemented by the joined discussion of recent scientific literature in the format of journal clubs. In computer laboratory sessions the students will implement the signal analysis algorithms introduced in the theory sessions. Furthermore, students will study in detail EEG recordings provided by our clinical partners. Finally, they will apply their signal analysis algorithms to these recordings and present their results in a session of oral presentations. See detailed study plan.
Computational Cardiology (second trimester)
This subject provides a detailed review of the different phases and concepts required for modelling the cardiovascular system in a realistic way. Special emphasis will be given to the generation of 3D patient-specific multi-scale and multi-physic simulations in healthy and pathological conditions, as well as to the in-silico testing of different therapeutic solutions. Additionally, at the beginning of the course there will be an extensive review of cardiac physiology and morphology. Then, the focus will be on the generation of detailed meshes integrating multi-modal data provided by different sources, setting up the geometrical domain where simulations will be run. The remaining lectures will be devoted to the presentation of the state-of-the-art modelling approaches to simulate the most relevant physical phenomena in cardiology: electrophysiology, (electro-)mechanics, and blood flow. See detailed study plan.
Computational Therapies (second trimester)
This subject will study the general architecture and implementation aspects of computer-assisted surgery, from planning and simulation to intraoperative navigation and surgical robotics. More specifically the following steps will be investigated: planning of pre-operative trajectories and structures; registration of pre-operative and intra-operative images; tracking of surgical instrumentation; augmented reality; biomechanical deformation models. Lectures will start by introducing the basic theory and existing technological choices. Then, we will analyse and discuss several real computer-assisted systems, also from the point of view of clinical applications. See detailed study plan.
Complexity Science (first trimester)
The objective of this course is to introduce the basic methods for the analysis of complex behavior in natural systems. Special emphasis will be made on the processes underlying complexity and collective dynamics in biological systems at all levels, ranging from the behavior of individual cells to populations of organisms and their associated social behavior. At the end of the course, the student will be able to interpret nature in terms of emergence and self-organization. See detailed study plan