MASTER

In Computational Biology

between ETSIAAB and ETSII

MÁSTER

En Biología Computacional

Entre ETSIAAB y ETSII

MÁSTER

En Biología Computacional

Entre ETSIAAB y ETSII

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Objectives & competences


Master's studies, in general, are intended for students that aim to acquire advanced training, of a specialized or multidisciplinary nature, of an academic or professional specialization, or to promote the initiation of research tasks in specific academic areas. It is in this last scenario where the present title of University Master's Degree in Computational Biology is located.

The objective of this Master program is to provide training mainly dedicated at Computational Biologists with a scientific profile, although an itinerary has been designed that can allow the acquisition of competencies in Bioinformatics Engineering with special emphasis on management, treatment and analysis of massive data (big data).

In this way, professionals in Biology and Informatics and, in general, professionals in Science and Technology, will be provided with a greater degree of knowledge in Computational Biology, so that they are trained to work in Research Centers and Research Groups in the field of Bioceonomy (plant biotechnology, agri-food, health, environmental, etc.) and in the field of computer science associated with databases and big data. Therefore, the training acquired in the master has an important transversal component, which opens up a wide range of professional possibilities for graduates.

Thus, the main goals of the program can be specified in the following specific objectives:

  • Objective 1. Acquire advanced knowledge and demonstrate, in a context of scientific and / or technological research, a detailed and well-founded understanding of the theoretical and practical aspects and of the work methodology in Computational Biology.
  • Objective 2. Provide a greater degree of knowledge in techniques and methods of Computational Biology to be able to tackle and solve problems of a scientific and technological nature.
  • Objective 3: To have developed sufficient autonomy to participate in research projects and scientific or technological collaborations within the field of Computational Biology, in interdisciplinary contexts and, where appropriate, with a high component of knowledge transfer.
  • Objective 4: To train the student to be creative when addressing and solving problems of a scientific and technological nature in the field of Computational Biology, and to assume responsibility for their own professional development.
Competencies
 
The objectives described above follow the aim that the students acquire a series of general and specific skills during their studies.
 
The competences of the University Master's Degree in Computational Biology have been structured in three categories.
  • The first includes general competences, some from a Royal Decree and, therefore, common to any Master's degree in Spain, other Master´s programs of the Universidad Politécnica de Madrid, and, finally, some included in the ANECA standard.
  • The second category of competences includes those related to the transversal orientation of the proposed degree, which reflect the objective of the master's degree to provide both research and professional training.
  • Finally, the third category includes the specific competencies in Computational Biology, which differentiate the proposed title and program from any other master's degree in the same area of ​​knowledge.

 

All these competences are covered throughout the study plan through subjects, seminars and the End of Master Project. The competences associated with each of the subjects of the degree are detailed below.
 
General competences
  • CG1. Possess the knowledge that constitutes the scientific and technological base of Computational Biology, which will allow the development of original ideas in this field, in a research or development context.
  • CG2. To become familiar with the work and methods of Computational Biology in real conditions, acquiring the ability to design applications / experiments independently and to describe, quantify, analyze and critically evaluate the results obtained.
  • CG3. That students know how to apply the acquired knowledge and their ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to the area of ​​Computational Biology.
  • CG4. That students are able to communicate the fundamentals of their lines of work in the area of ​​Computational Biology, as well as the results and conclusions obtained, to specialized and non-specialized audiences in a clear and unambiguous way.
  • CG5. That students are able to integrate knowledge in the area of ​​Computational Biology, to formulate conclusions, hypotheses or lines of work from the available information, and to form an informed opinion about the social and ethical responsibilities linked to the application of their knowledge.
  • CG6. That students possess the learning skills that allow them to continue studying in a way that will have to be largely self-directed or autonomous to adapt to the anticipated rapid evolution in the area of ​​Computational Biology.
Transversal competences
  • CT1. Ability to professionally apply the knowledge acquired to their work considering its impacts in a global and social context.
  • CT2. Ability to apply the scientific method to solve problems effectively and creatively.
  • CT3. Have a bioethical and professional commitment and respect for environmental sustainability.
  • CT4. Ability to communicate to all types of audiences in the English language, both orally and in writing.
  • CT5. Ability to write technical documents, and organize and plan experiments and, in general, work of a professional nature.
  • CT6. Ability to lead and work in multidisciplinary and multicultural teams in an international context.
  • CT7. Being able to handle information and communication technologies in a professional context.
  • CT8. Have the capacity for analysis and synthesis to interpret relevant data and approach problems from different perspectives.
Specific skills
  • CE1. Understand the molecular bases and the most common standard experimental techniques in omics research (genomics, transcriptomics, proteomics, metabolomics, interactomics, etc.).
  • CE2. Use operating systems, programs and tools commonly used in computational biology, as well as handle high-performance computing platforms, programming languages ​​and bioinformatics analysis.
  • CE3. Analyze and interpret bioinformatics data derived from omics technologies and propose bioinformatics solutions to problems derived from research with said data.
  • CE4. Use different databases (including big data), know their structures and ontologies, apply statistics to their analysis, being able to use representation and visualization tools.
  • CE5. Use computational biology tools for genomic analysis, including comparative genomics and evolutionary biology.
  • CE6. Identify the bioinformatics needs of research centers and companies in the biotechnology and biomedicine sector.
  • CE7. Apply the knowledge acquired to carrying out scientific-technological work in the field of Computational Biology, Bioinformatics and big data.
  • CE8. Ability to integrate technologies and systems of Artificial Intelligence, with a general nature, and in broader and multidisciplinary contexts.
  • CE9. Ability to interpret the supervised and unsupervised classification models obtained by applying Machine Learning techniques to a set of data.
  • CE10. Knowledge of reusable knowledge representation techniques and reasoning models in centralized and distributed environments to be used in solving problems involving intelligent behavior.