The program's Study program has 60 ECTS credit
26 ECTS credit - work end of Master's Degree
22 ECTS credit - Compulsory subjects
12 ECTS credit - Electives
MODULES, SUBJECTS AND SUBJECTS
| MODULE | SUBJECT | SUBJECT | OP/OB | ECTS CREDIT |
| 1 work End of Master's Degree |
1.1 |
work End of Master's Degree (TFM) |
OB | 26 |
| 2 General |
2.1 |
Ethics of scientific praxis |
OB | 2 |
| 2.2 Science of data and models in experimental sciences. |
Programming for science of data |
OB | 5 | |
|
Numerical modelling in experimental sciences |
OB | 5 | ||
| 2.3 Introduction to Bioinformatics |
Advanced statistical methods |
OB | 5 | |
|
Machine learning I |
OB | 5 | ||
| 3 Elective |
3.1 Electives |
Image processing |
OP | 3 |
|
Data acquisition |
OP | 3 | ||
|
Advanced programming |
OP | 3 | ||
|
Analysis and interpretation of data from high performing |
OP | 3 | ||
|
Machine learning II |
OP | 3 | ||
|
Sequence analysis and structural bioinformatics |
OP | 3 | ||
|
Deep learning |
OP | 3 | ||
|
data analysis in biology |
OP | 3 | ||
|
management from data experimental |
OP | 3 | ||
|
data analysis at Chemistry |
OP | 3 | ||
|
Analysis and procedure of data spatial |
OP | 3 |
project end of Master's Degree
The Master's Degree has an orientation internship: from the first day, each student will start a project on his or her area of interest to apply in a real case the IT tools explained in the subjects.
The TFM (26 ECTS credit) is a work directed by a tutor, who must first meet with the student. It begins on the first day of the course and constitutes the pivot around which the Master's Degree revolves.
Many researchers at the University of Navarra and research center Applied Medicine (CIMA) offer TFM degrees that they are willing to supervise. Alternatively, the student can look for a professor willing to supervise a work of his/her interest and propose it as a TFM. In addition, there are agreements in place with companies interested in hosting a student from Master's Degree to conduct their TFM at business.
SOME EXAMPLES
Automated image analysis diagnostic system
The project aims to develop a low-cost diagnostic kit to detect, by means of image analysis on a biological sample , certain diseases that are particularly prevalent in poor countries (tuberculosis, malaria, etc.).
Analysis of the sensitivity of different species distribution models to the degradation of biological and environmental information. available
To know the limitations that the most frequently used methods in the literature have on the quality of the information on entrance, in order to decide which is the most appropriate tool to use depending on the primary data available.
Precision personalised medicine for the treatment of multiple myeloma using Deep Learning techniques
In this project we propose to use data of RNA-Seq, DNA-Seq and clinical data to design a new stratification system for multiple myeloma patients based on the employment of autoencoders.
Modelling catalytic photodegradation kinetics in aqueous media
The work will consist of developing general kinetic models of photodegradation that take into account other substances present in the aqueous medium.
