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Master's Degree Dissertation

Code: 42257
Credits: 12
2026/2027
Degree programme Type Course
Modelling for Science and Engineering TFE 1

Contact lecturer

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

Group languages

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

Prerequisites

None

Objectives

The Master's Thesis is intended to enable students to demonstrate, in an integrated manner, the knowledge, skills, and competencies acquired throughout the master's program.

The final objective is to deliver a public presentation to defend the written report on a topic related to each student's area of specialization: Modeling for Science, Data Science, Mathematical Modeling, or Modeling for Engineering, under the supervision of an expert in the corresponding field.

Learning outcomes

  • CA33 (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.) 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.
  • CA34 (Communicate, both to an expert and a general audience, the procedures and results obtained during the preparation of the Master's Dissertation.) Communicate, both to an expert and a general audience, the procedures and results obtained during the preparation of the Master's Dissertation.
  • CA35 (Assess the potential ethical, sustainability, gender equality and/or social justice implications associated with the Master's Dissertation and/or its subject matter.) Assess the potential ethical, sustainability, gender equality and/or social justice implications associated with the Master's Dissertation and/or its subject matter.
  • KA25 (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.) 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.
  • KA26 (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.) 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.
  • SA34 (Use specific and/or self-developed software to study a specific process, in the context of preparing the Master's Dissertation.) Use specific and/or self-developed software to study a specific process, in the context of preparing the Master's Dissertation.
  • SA35 (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.) 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.
  • SA36 (Interpret the results obtained during the Master's Dissertation proposed.) Interpret the results obtained during the Master's Dissertation proposed.

Contents

There are not theoretical contents for this module.

Learning activities and methodology

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

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 the oral presentation will be fixed between June 21 and June 28.

During March 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 14 at 11.59 p.m.

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

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Oral dissertation 20% 0 0 CA33, CA34, CA35, KA25, KA26, SA34, SA35, SA36
Contents of the work 50% 0 0 CA33, 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. 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.


The final grade will be distributed as follows: 80% for the written report (content and writing quality) and 20% for the oral presentation.

In this course, the use of Artificial Intelligence (AI) technologies is permitted exclusively for support tasks, such as literature or information searches and text editing. Students must clearly identify which parts have been generated using these technologies, specify the tools employed, and include a critical reflection on how they have influenced both the process and the final outcome of the assignment. Failure to disclose the use of AI in this assessed activity will be considered a breach of academic integrity and may result in a partial or total reduction of the assignment grade, or more severe disciplinary sanctions in cases of serious misconduct.

Bibliography

There are no specific references.

Software

There is no specific software.

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TFMm) Treball de fi de màster 1 English second semester afternoon