Graduate School of Engineering and Basic Sciences
- Direction
- Prof. Vanessa Gómez Verdejo
- Language
- English
- Attendance
- On-campus
- Credits
- 60
- Campus
- Leganes
- Applications
-
CLOSED
☛ Places available: 40
Double Degree: Open
- Departments
- Signal and Communications Theory Department, Bioengineering Department
CONTACT
Click here and make your query
APPLICATION FOR ADMISSION
If you do not remember your password you can access our application to create a new one.
- Inicio
The Master in Machine Learning for Health (formerly Master in Information Health Engineering) emerges as an answer to the increasing demand of researchers with an interdisciplinary background in the fields of machine learning and bioengineering. Nowadays, the intersection of these two areas stands out for its enormous potential in both research and application: the role of machine learning, signal processing, data science, and artificial intelligence is becoming crucial in almost any field and particularly in health applications. Significantly, both public and private investing in research related to these areas has an enormous social and economic impact. In fact, companies such as Philips, Siemens, Microsoft, IBM, Amazon, Google or Apple, to name just a few, are demanding this research profile.
This Master combines the disciplines of machine learning and health with the goal of training researchers to become experts in signal and data analysis tools, with special emphasis on their use on medical signals and images. The training provided by the master's degree will have a strong theoretical foundation, which will provide future graduates with the necessary knowledge to start their subsequent doctoral studies and/or develop R&D activities in industry.
│MASTER IN NUMBERS
- ☛ Taught by more than 20 professors and leading researchers in the field
- ☛ The program is completed in one academic year
- ☛ According to Forbes two of the three most demanded jobs are related to AI and health
- ☛ Personalized master program: an offer of 18 courses to adjust the program to your background and interests
- CURRICULUM
- CURRICULUM
The program consists of 60 ECTS to be studied in 2 semesters with the following structure:
SEMESTER 1 (30 ECTS)
- SUBJECT 1 | BASIC FORMATION
Formed by 3 compulsory subjects of 6 ECTS each. - SUBJECT 2 | METHODS AND TOOLS FOR COMPUTATIONAL INTELLIGENCE
It contains 4 elective subjects of 6 ECTS each. Students must choose two of these four subjects.
SEMESTER 2 (30 ECTS)
- SUBJECT 3 | MEDICAL IMAGING AND COMPUTER VISION
Composed of several elective subjects of 3 and 6 ECTS on the processing and analysis of data based on medical images. - SUBJECT 4 | LEARNING MACHINE IN HEALTH
Composed of several elective subjects of 3 and 6 ECTS on advanced methods of machine learning relevant in specific areas of health. - SUBJECT 5 | RESEARCH METHODS
With a mandatory subject of 3 ECTS associated with research skills. - SUBJECT 6 | MASTER THESIS
Formative Complements ** Subjects ECTS TYPE Language Introduction to Biosignals and Bioimaging 3 FC 
Introduction to Machine Learning 2 FC Introduction to Statistical Signal Processing 2 FC Year 1 - Semester 1
CORE COURSES Subjects ECTS TYPE Language Biosignals & Bioimages 6 C Machine Learning 6 C Statistical Signal Processing 6 C METHODS AND TOOLS FOR COMPUTATIONAL INTELLIGENCE (choose 2) Subjects ECTS TYPE Language Deep Learning 6 E Biomedical Image Processing 6 E Optimization 6 E Year 1 - Semester 2
MEDICAL IMAGING AND COMPUTER VISION* (choose a minimum of 6 ECTS) Subjects ECTS TYPE Language Medical image reconstruction 6 E Surgical navigation and imaging 3 E Neuroimaging 3 E Computer Vision 6 E Artificial Intelligence in radiology and microscopy 3 E MACHINE LEARNING FOR HEALTH* (choose a minimum of 6 ECTS) Subjects ECTS TYPE Language Information Theory for Machine Learning 6 E Probabilistic and Generative Machine Learning 6 E Natural Language Processing 6 E Personalized medicine 3 E Speech technologies for health 3 E RESEARCH METHODS Subjects ECTS TYPE Language Reseach Skills 3 C MASTER'S THESIS Subjects ECTS TYPE Language Master's Thesis 9 TFM vacio * To complete the 30 ETCS of the Semester 2, the students must choose a total of 18 ECTS between the subjects of the subject-matter 3 and 4, choosing a minimum of 6 ECTS in each subject-matter.
** Formative Complements: in general, according to the entry profile, the following is established:
- Students coming from Data Science and Telecommunication Engineering degrees must take Subject 1.
- Students coming from Bioengineering degrees must take Subjects 2 and 3.
- Students coming from Computer Engineering degrees must take Subjects 1 and 3.
The rest of the admission profiles must take the three training complements. However, the academic committee of the master's degree will be responsible for evaluating the profile of the students, considering the curriculum taught in the center of origin and the specific training of each one, and may assign in some cases additional complements or exempt the completion of some of them.
C) Compulsory: 21 ECTS
E) Elective course: 30 ECTS
TFM) Master Thesis: 9 ECTS
course Programs
- SUBJECT 1 | BASIC FORMATION
- QUALITY
GENERAL COURSE INFORMATION
☛ First year offered: 2019
PROGRAMME’S QUALITY ASSURANCE
The Academic Committee of the Master’s programme complies with the SGIC-UC3M and it is responsible for the follow-up, analysis, review, assessment and quality of the program, it contributes with proposals to improve the program and produces the “Memoria Académica de Titulación” (Programme Report).
FACULTY AND COURSE PLAN
Graduate Profile and Competences
- CURRICULUM
- FACULTY
FACULTY
The Master in Machine Learning for Health has a teaching team of full professors and associate professors from the different Departments participating in the program:
UC3M FACULTY
- ABELLA GARCÍA, MÓNICA
Department of Bioengineering
Associate Professor
PhD / Engineer
Brief CV
- ARENAS GARCÍA, JERÓNIMO
Department of Signal Theory and Communications
Full Professor
PhD
Brief CV
- ARTÉS RODRÍGUEZ, ANTONIO
Department of Signal Theory and Communications
Full Professor
PhD
Brief CV
- CID SUEIRO, JESÚS
Department of Signal Theory and Communications
Full Professor
PhD
Brief CV
- DESCO MENÉNDEZ, MANUEL
Department of Bioengineering
Full Professor
PhD / Engineer
Brief CV
- DÍAZ DE MARÍA, FERNANDO
Department of Signal Theory and Communications
Full Professor
PhD
Brief CV
- GARCÍA SEVILLA, MONICA
Department of Bioengineering
PhD Assistant Professor
PhD
Brief CV
- GONZÁLEZ DÍAZ, IVÁN
Department of Signal Theory and Communications
Associate Professor
PhD
Brief CV
- GÓMEZ VERDEJO, VANESSA
Department of Signal Theory and Communications
Associate Professor
PhD
Brief CV
- IZQUIERDO GARCÍA, DAVID
Department of Bioengineering
- KOCH, TOBIAS
Department of Signal Theory and Communications
Associate Professor
PhD
Brief CV
- LANCHO SERRANO, ALEJANDRO
Department of Signal Theory and Communications
Postdoctoral Researcher
- MARTÍNEZ OLMOS, PABLO
Department of Signal Theory and Communications
Associate Professor
PhD
Brief CV
- MÍGUEZ ARENAS, JOAQUÍN
Department of Signal Theory and Communications
Full Professor
PhD
Brief CV
- MOLINA BULLA, HAROLD
Department of Signal Theory and Communications
Visiting Professor
PhD
Brief CV
- MUÑOZ BARRUTIA, ARRATE
Department of Bioengineering
Full Professor
PhD / Engineer
Brief CV
- PARRADO HERNÁNDEZ, EMILIO
Department of Signal Theory and Communications
Associate Professor
PhD
Brief CV
- PASCAU GONZÁLEZ-GARZÓN, JAVIER
Department of Bioengineering
Full Professor
PhD / Engineer
Brief CV
- PELÁEZ MORENO, CARMEN
Department of Signal Theory and Communications
Associate Professor
PhD
Brief CV
- RAMÍREZ GARCÍA, DAVID
Department of Signal Theory and Communications
Associate Professor
PhD
Brief CV
- RIOS MUÑOZ, GONZALO RICARDO
Department of Bioengineering
PhD Assistant Professor
PhD
Brief CV
- SEVILLA SALCEDO, CARLOS
Department of Signal Theory and Communications
Visiting Professor
PhD
Brief CV
- VAQUERO LÓPEZ, JUAN JOSÉ
Department of Bioengineering
Full Professor
PhD / Engineer
Brief CV
- VÁZQUEZ VILAR, GONZALO
Department of Signal Theory and Communications
Associate Professor
PhD
Brief CV
- ABELLA GARCÍA, MÓNICA
- ADMISSION
- ADMISSION
Application
The request must be submitted electronically through our application system. Before beginning the admission process, please read the following information:
REQUIREMENTS
To access this master's degree, it is necessary to hold a degree in Telecommunications Engineering, Data Engineering and Data Science, Computer Science and Computer Engineering, Electrical Engineering, Biomedical Engineering or Bioengineering
FORMATIVE COMPLEMENTS
Formative Complements are defined to facilitate the incorporation of students with different profiles, with emphasis on those profiles without knowledge of Bioengineering, Machine Learning and Statistics. It is a block composed of three courses with contents to acquire the necessary competences to start the master's program, which will be taken according to the profile and previous knowledge of the applicants.
The dates and schedules of the Formative Complements can be consulted here.
ADMISSION CRITERIA
Candidate selection will be done on the basis of the following criteria:
ADMISSION CRITERIA SCORING Academic record 60% Research experience 10% Grades in essential courses to the Master's programme 15% Motivation, interest and recommendation letters 10% Professional experience and other academic merits (awards, grants, international stays, etc.) 5% ADMISSION PROFILE
The Master of Information Health Engineering is mainly aimed at engineering graduates of the following families:
- Telecommunications Engineering
- Data Engineering and Data Science
- Computer Science and Computer Engineering
- Electrical Engineering
- Biomedical Engineering or Bioengineering
or other related degrees such as mathematics or other engineering degrees
Language requirements
Check the general language requirements required to study a Master’s at UC3M, depending on whether it is in Spanish, English or bilingual.
students with foreign university degrees
Once admitted to the Master’s program, students holding a university degree from a higher education institution outside the EHEA must provide the diploma, legalized through diplomatic procedures or by The Hague Apostille, for enrollment. They must also submit their transcript of records, including the grade point average, duly legalized.
More information about Legalization of Foreign Documents.
If needed, documents must be accompanied by an official sworn translation into Spanish.
- ENROLLMENT
TUITION FEES*
Reservation fee: €450
- it will be paid once the student receives notification of admission to the master’s, and deducted from the first tuition payment
- the reservation fee will only be refunded if the master program is cancelled
EU STUDENTS (€45.02/ECTS) First academic year - 60 ECTS €2,701.20 NON EU STUDENTS (€84.07/ECTS) First academic year - 60 ECTS €5,044.20 NOTE: the indicated public prices do not include in any case, neither the ECTS corresponding to the Formative Complements that the student must take (only master's degrees with previous Formative Complements), nor the cost of issuing the master’s degree certificate.
_______
* Current fees for the 24/25 academic year, pending approval by the Community of Madrid for the 25/26 academic year.
Additional information
- You may enrol on the master’s degree after completing the admission process and receiving formal confirmation of your acceptance.
- When performing the enrolment you can choose between Full-time enrolment or Part-time enrolment.
- The email address provided upon enrolment will be used for formal communications; students are therefore kindly requested to check their mail regularly.
- Pursuant to the regulations of the Universidad Carlos III de Madrid, a student failing to pay any part of the fees will not be admitted and the enrolment process will be terminated. In cases of cancellation of enrolment due to non-payment, the University may demand the payment of the pending amounts for enrolment in previous academic courses as a prior condition of enrolment.
No diploma or certificate will be issued if a student has any outstanding payments.
- ADMISSION
- SCHOLARSHIPS
UC3M FULL SCHOLARSHIPS TO THE MASTER'S STUDY FOR THE ACADEMIC YEAR 2025/2026
CALL AEM_UC3M-25/26 │ CLOSED
The Carlos III University of Madrid announces 2 full scholarships to study the Master in Machine Learning for Health in the academic year 2025/2026, which gives access to doctoral programs of the UC3M.
- The full scholarships will include the prices corresponding to the ECTS of the first registration of the complete course, as well as an allowance of €10,000.
To apply for the scholarship it is necessary to have formalized the application for admission to the master's program.
Two periods are established for the submission of applications:
- Ordinary period: from the day following the publication of this call on the University's website, until March 31, 2025.
- Extraordinary period (for programs with vacancies): from April 10 to May 31, 2025.
Resolution: to be published in the BOEL and the web page of this call for proposals
_______
General information on scholarships
For more information on specific scholarships of interest, awarded by the Universidad Carlos III de Madrid as well as other agencies or organizations, please refer here:
- PRACTICAL INFORMATION
MASTER’S COURSE SCHEDULE
_______
FORMATIVE COMPLEMENTS
📌 Formative Complements for the 2025/26 academic year will be taught as follows:
- ONLINE: 29/08 | 9:00-20:00 h.
- ON-SITE: 1/09-5/09 | M-F | 9:00-20:00 h.
- DOUBLE DEGREE
- CURRICULUM
DOUBLE MASTER DEGREE IN TELECOMMUNICATIONS ENGINEERING AND MACHINE LEARNING FOR HEALTH
STRUCTURE
The Double Master's Degree Curriculum is structured around two academic years in which you will take 72 ECTS from the Master's Degree in Telecommunications Engineering and 48 ECTS from the Master's Degree in Machine Learning for Health.
The 120 total credits from the dual curriculum is broken down as follows:
- In the first year, students take subjects for the Master's Degree in Telecommunications Engineering (30 ECTS in each semester). Each semester you will take five core subjects of 6 ECTS each.
- During the second year of teaching, you will take specialty subjects in Machine Learning for Health (9 compulsory ECTS and 30 elective ECTS), and the 2 Master's Thesis corresponding to each master's degree.
You will have 30 validated ECTS credits, and on completion you receive two Official Master's Degree qualifications.
Year 1 - Semester 1
Master in Telecommunications Engineering Subjects ECTS TYPE Language Design and Operation of Communication Networks 6 C 
Advanced Multimedia Services 6 C Electronic Circuit Design for Communication 6 C Radio Frequency and Antenna Subsystems 6 C Data Processing 6 C Year 1 - Semester 2
Master in Telecommunications Engineering Subjects ECTS TYPE Language Design of Telematics Applications 6 C Electronic Instrumentation and Optoelectronics 6 C Design and Simulation of Communication Systems 6 C Advanced Techniques in Signal Processing and Communications 6 C Project Management and Telecommunications Policy 6 C Year 2 - Semester 1
Master in Machine Learning for Health Subjects ECTS TYPE Language Core Courses Biosignals & Bioimages 6 C Methods and tools for computational intelligence
(Choose 2)Deep Learning 6 E Biomedical Image Processing 6 E Optimization 6 E Master in Telecommunications Engineering Subjects ECTS TYPE Language Master Thesis Master's Thesis. Telecommunications Engineering 12 TFM vacio Year 2 - Semester 2
Master in Machine Learning for Health Subjects ECTS TYPE Language Medical imaging and computer vision*
(Choose at least 6 ECTS)Medical image reconstruction 6 E Surgical navigation and imaging 3 E Neuroimaging 3 E Computer Vision 6 E Artificial Intelligence in radiology and microscopy 3 E Machine learning for health*
(Choose at least 6 ECTS)Information Theory for Machine Learning 6 E Probabilistic and Generative Machine Learning 6 E Natural Language Processing 6 E Personalized medicine 3 E Speech technologies for health 3 E Research methods Reseach Skills 3 C Master Thesis Master's Thesis. Machine Learning and Health 9 TFM vacio * A total of 18 elective ECTS must be completed, choosing a minimum of 6 ECTS among the subjects indicated.
C) Compulsory
E) Elective
TFM) Master's Thesis
course Programs
- ADMISSION
Application
The request must be submitted electronically through our application system. Before beginning the admission process, please read the following information:
Requirements
Check the requirements of the Master in Telecommunications Engineering.
Check the requirements of the Master in Machine Learning for Health.
Admission criteria
Check the admission criteria of the Master in Telecommunications Engineering.
Check the admission criteria of the Master in Machine Learning for Health.
Language requirements
Check the general language requirements required to study a Master’s at UC3M, depending on whether it is in Spanish, English or bilingual.
STUDENTS with foreign university degrees
Check the requirements of the Master in Telecommunications Engineering for students with foreign university degrees.
Check the requirements of the Master in Machine Learning for Health for students with foreign university degrees.
Important
The admission process is carried out by the commission of each master independently and therefore, to access the double degree, it is necessary to have been previously admitted in both master's degrees
- ENROLLMENT
TUITION FEES*
DOUBLE MASTER's DEGREE IN Telecommunications Engineering AND MACHINE LEARNING FOR HEALTH Reservation fee1 €900 Price EU students €32.22 / ECTS Price Non EU students €119.44 / ECTS Credits enrolling 120 ECTS Credits that are recognized (without cost)2 Telecommunications Engineering 18 ECTS Credits that are recognized (without cost)2 Machine Learning for Health 12 ECTS 1 Admission to the double master's program implies payment of the reservation fee within 10 calendar days of receiving notification of admission (this amount will be deducted from the first tuition payment). The reservation fee will only be refunded if the master program is cancelled. [+] information
NOTE: the indicated public prices do not include in any case, neither the ECTS corresponding to the Formative Complements that the student must take (only master's degrees with previous Formative Complements), nor the cost of issuing the master’s degree certificate.
IMPORTANT
2 Admission and registration on the double master's degree course will provide exemption from ECTS credits which will be recognized according to the curriculum (see Program). Having obtained credits from both syllabuses, together with recognition of 30 ECTS you will be able to obtain two official master's qualifications in little more than a year and a half.
☛ Enrollment completely registers the student in the Master's Degree.
☛ A minimum enrollment of 25 students will have to be reached for each elective subject to be taught.
_______
* Current fees for the 24/25 academic year, pending approval by the Community of Madrid for the 25/26 academic year.
Additional information
- You may enrol on the master’s degree after completing the admission process and receiving formal confirmation of your acceptance.
- When performing the enrolment you can choose between Full-time enrolment or Part-time enrolment.
- The email address provided upon enrolment will be used for formal communications; students are therefore kindly requested to check their mail regularly.
- Pursuant to the regulations of the Universidad Carlos III de Madrid, a student failing to pay any part of the fees will not be admitted and the enrolment process will be terminated. In cases of cancellation of enrolment due to non-payment, the University may demand the payment of the pending amounts for enrolment in previous academic courses as a prior condition of enrolment.
No diploma or certificate will be issued if a student has any outstanding payments.
- PRACTICAL INFORMATION
Academic Calendar
Academic Calendar 2024-2025 - School of Engineering
_______
MASTER’S COURSE SCHEDULE
Master in Telecommunications Engineering timetable:
VIRTUAL SECRETARIAT
Access to Virtual Secretariat (Higher Polytechnic School)
Access to Virtual Secretariat (Leganés)
- CURRICULUM