This version of the course guide is provisional until the period for editing the new course guides ends.

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Master's Degree Dissertation

Code: 42257 ECTS Credits: 12
2025/2026
Degree Type Year
Modelización para la Ciencia y la Ingeniería / Modelling for Science and Engineering TFE 1

Contact

Name:
Silvia Cuadrado Gavilan
Email:
silvia.cuadrado@uab.cat

Teachers

Ana Cortes Fite

Teaching groups languages

You can view this information at the end of this document.


Prerequisites

None


Objectives and Contextualisation

The objective of the master's thesis is to prepare students for future work, whether to carry out a doctoral thesis or to work in a company or research center. The final goal is to make a public presentation to defend the written report (Master's thesis) on a topic depending on the specialization of each student: Modeling for science, Data science, Mathematical modeling, or Modeling for engineering, under the guidance of an expert in the field.


Learning Outcomes

  1. CA33 (Competence) Critically assess the possibility of applying the tools used throughout the Master's Dissertation to respond to more general problems in the industrial or research field.
  2. CA33 (Competence) Critically assess the possibility of applying the tools used throughout the Master's Dissertation to respond to more general problems in the industrial or research field.
  3. CA33 (Competence) Critically assess the possibility of applying the tools used throughout the Master's Dissertation to respond to more general problems in the industrial or research field.
  4. CA34 (Competence) Communicate, both to an expert and a general audience, the procedures and results obtained during the preparation of the Master's Dissertation.
  5. CA35 (Competence) Assess the potential ethical, sustainability, gender equality and/or social justice implications associated with the Master's Dissertation and/or its subject matter.
  6. CA35 (Competence) Assess the potential ethical, sustainability, gender equality and/or social justice implications associated with the Master's Dissertation and/or its subject matter.
  7. KA25 (Knowledge) Recognise those programming environments necessary to study the problems associated with a particular project in order to propose the structure of the Master's Dissertation.
  8. KA26 (Knowledge) Identify the optimisation techniques and machine learning, as well as the computing architectures and mathematical tools required to solve the problem studied in the Master's Dissertation.
  9. KA26 (Knowledge) Identify the optimisation techniques and machine learning, as well as the computing architectures and mathematical tools required to solve the problem studied in the Master's Dissertation.
  10. KA26 (Knowledge) Identify the optimisation techniques and machine learning, as well as the computing architectures and mathematical tools required to solve the problem studied in the Master's Dissertation.
  11. KA26 (Knowledge) Identify the optimisation techniques and machine learning, as well as the computing architectures and mathematical tools required to solve the problem studied in the Master's Dissertation.
  12. SA34 (Skill) Use specific and/or self-developed software to study a specific process, in the context of preparing the Master's Dissertation.
  13. SA34 (Skill) Use specific and/or self-developed software to study a specific process, in the context of preparing the Master's Dissertation.
  14. SA35 (Skill) Critically identify the advantages, disadvantages and/or limitations of mathematical and/or optimisation techniques in the construction of models used to develop the Master's Dissertation.
  15. SA35 (Skill) Critically identify the advantages, disadvantages and/or limitations of mathematical and/or optimisation techniques in the construction of models used to develop the Master's Dissertation.
  16. SA36 (Skill) Interpret the results obtained during the Master's Dissertation proposed.

Content

There are not theoretical contents for this module.


Activities and Methodology

Title Hours ECTS Learning Outcomes
Type: Supervised      
Regular meetings with the supervisor 25 1 CA33, CA34, CA35, KA25, KA26, SA34, SA35, SA36, CA33
Type: Autonomous      
Elaboration of the report 275 11 CA33, CA35, KA25, KA26, SA34, SA35, SA36, CA33

During the first semester, some thesis/project offers will be published. Students may also submit their own project proposals to the coordinator. Students can carry out the project at a university, research center, and, in some cases, at a company. Once the topic and supervisor have been assigned, the student and the supervisor will meet regularly during the second semester.

Concerning the report and the dissertation of the Master Thesis.

General guidelines: the report should be between 30 and 50 pages long and should contain:

-        A first page with the title, author's name, director's name, date.

-        Abstract

-        Acknowledgements

-        Contents

-        List of Figures, Tables, (if necessary)

-        Introduction chapter

-        Other chapters.

-        Conclusions

-        Bibliography

We recall that any paragraph taken from the Internet or from existing books must be written between quotation marks " " and carefully referencing the source.

For the presentation each student will have between 25 and 30 minutes to focus the question, lay the objectives, explain and put the results in context, and present the conclusions. Afterwords the jury will have a maximum of 30 minutes to ask questions and discuss with the student.

Calendar

The main period for thepresentation will be fixed between June 29 and July 3.

During March/April, a task will be enabled in the CV where each student  must inform  the title, the advisor and the abstract of the Master's Degree.

Delivering the Master Thesis.

Each student must send the TFM report to the subject's virtual campus before June 21 at 23.59.

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.


Assessment

Continous Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Contents of the work 50% 0 0 CA33, CA35, KA25, KA26, SA34, SA35, SA36
Oral dissertation 20% 0 0 CA33, CA34, CA35, KA25, KA26, SA34, SA35, SA36
Written report 30% 0 0 CA33, CA35, KA25, KA26, SA34, SA35, SA36

The master's thesis will be evaluated by a committee of three members. The thesis supervisor or a member of their team may be part of the committee but should not chair it. At least one member of the committee must belong to the UAB. Once the members have accepted to be part of the committee, the date and time of the defense are scheduled, aligning with the proposed period

The grade will be divided as follows: 30% for the written report, 20% for the oral dissertation and 50% for the work itself.


Bibliography

There are no specific references.


Software

There is no specific software.


Groups and Languages

Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.