thesis upm.es

• JCR Articles
• Proceedings
• Technical Reports
• Presentations
• Quesada (2023). Multivariate Time-Series Modelling and Forecasting with High-Order Dynamic Bayesian Networks Applied in Industrial Settings . Ph.D. in Computer Science. Technical University of Madrid
• E. Puerto-Santana (2023). Asymmetric Hidden Markov Models and Extensions Applied to Industry . Ph.D. in Computer Science. Technical University of Madrid
• Rodríguez Sánchez, Fernando (2021).  Multidimensional clustering with Bayesian networks. Tesis (Doctoral), E.T.S. de Ingenieros Informáticos (UPM). DOI
• Atienza González, David (2021).  Nonparametric Models and Bayesian Networks. Applications to Anomaly Detection. Tesis (Doctoral), E.T.S. de Ingenieros Informáticos (UPM). DOI
• Córdoba Sánchez, Irene (2020).  Unifying methodologies for graphical models with Gaussian parametrization. Tesis (Doctoral), E.T.S. de Ingenieros Informáticos (UPM). DOI
• Benjumeda, M.,  Learning Tractable Bayesian Networks,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2019
• Diaz-Rozo, J.,  Clustering probabilístico dinámico para la búsqueda de patrones de degradación de elementos de máquina en el ámbito de la Industria 4.0,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2019.
• Fernandez-Gonzalez, P.,  Developments in probabilistic graphical models, circular distributions and theory of random forests with applications in neuroscience , , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2019.
• Luengo-Sanchez, S.,  Clustering based on Bayesian networks with Gaussian and angular predictors : applications in Neuroscience,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2019.
• Leguey, I.,  Directional-linear Bayesian networks and applications in neuroscience,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2018.
• Mihaljevic, B.,  Contributions to Bayesian network classifiers and interneuron classification,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2018.
• Varando, G.,  Theoretical studies on Bayesian network classifiers,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2018.
• Anton-Sanchez, L..,  “Statistical and optimization methods for spatial data analysis applied to neuroscience”,  (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid., 2017.
• Ibáñez, A.,  Machine Learning in Scientometrics,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2015.
• Borchani, H.,  Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data , , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2013.
• Karshenas, H.,  Regularized Model Learning in EDAs for Continuous and Multi-Objective Optimization,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2013.
• Lopez-Cruz, P. L.,  Contributions to Bayesian network learning with applications to neuroscience,  , (supervised by C. Bielza and P. Larrañaga), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2013.
• Guerra, L.,  Semi-supervised subspace clustering and applications to neuroscience,  , (supervised by C. Bielza and V. Robles), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2012.
• Vidaurre, D.,  Regularization for sparsity in statistical analysis and machine learning , , (supervised by Pedro Larrañaga and Concha Bielza), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2012.
• Correa, M.,  Inteligencia Artificial para Predicción y Control del Acabado Superficial en Procesos,  , (supervised by Concha Bielza), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2010.
• Miquelez, T.,  Avances en Algoritmos de Estimación de Distribuciones. Alternativas en el Aprendizaje y Representación de Problemas,  , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2010.
• Pérez, A.,  Supervised Classification in Continuous Domains with Bayesian Networks,  , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2010.
• Armañanzas, R.,  Consensus Policies to Solve Bioinformatic Problems Through Bayesian Network Classifiers and Estimation of Distribution Algorithms,  , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2009.
• Morales, D.,  Clasificadores Bayesianos en la Selección Embrionaria en Tratamientos de Reproducción Asistida , , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2009.
• Calvo, B.,  Positive Unlabelled Learning with Applications in Computational Biology , , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2008.
• Santafé, G.,  Advances on Supervised and Unsupervised Learning of Bayesian Networks Models. Applications to Population Genetics,  , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2008.
• Romero, T., Algoritmos de Estimación de Distribuciones Aplicados a Problemas Combinatorios en Modelos Gráficos Probabilísticos, , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2007.
• Fernandez del Pozo, J. A.,  Listas KBM2L para la Síntesis de Conocimiento en Sistemas de Ayuda a la Decisión,  , (supervised by Concha Bielza), Facultad de Informática, Politécnica de Madrid, 2006.
• González, C.,  Contributions on Theoretical Aspects of Estimation of Distribution Algorithms,  , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2006.
• Santana, R.,  Advances in Probabilistic Graphical Models for Optimization and Learning. Applications in Protein Modelling , , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasc, 2006.
• Blanco, R., Learning Bayesian Networks from Data with Factorization and Classification Purposes. Applications in Biomedicine, , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2005.
• Merino, M., Predicción de Mortalidad Precoz tras Implantación Percutánea Intrahepática en Pacientes Cirróticos. Aplicación de Métodos de Clasificación Supervisada, , (supervised by Pedro Larrañaga), Departamento de Medicina Interna, Universidad de Navarra, 2004.
• Robles, V.,  Clasificación Supervisada basada en Redes Bayesianas. Aplicación en Biología Computacional,  , (supervised by Pedro Larrañaga), Departamento de Arquitectura y Tecnología de Sistemas Informáticos, Universidad Politécnica de Madrid, 2003.
• Bengoetxea, E.,  Inexact Graph Matching Using Estimation of Distribution Algorithms,  , (supervised by Pedro Larrañaga), Département Traitement du Signal et des Images, Ecole Nationale Supérieure de Télécomunications, 2002.
• Gómez, M., IctNeo: Un Sistema de Ayuda a la Decisión para el Tratamiento de la Ictericia, , (supervised by Concha Bielza), Departamento de Inteligencia Artificial, Universidad Politécnica de Madrid, 2002.
• Inza, I., Advances in Supervised Classification Based on Probabilistic Graphical Models, , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2002.
• Peña, J. M., On Unsupervised Learning of Bayesian Networks and Conditional Gaussian Networks, , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2001.
• Sierra, B., Aportaciones Metodológicas a la Clasificación Supervisada, , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 2000.
• Lozano, J. A., Algoritmos Genéticos Aplicados a la Clasificación no Supervisada, , (supervised by Pedro Larrañaga), Departamento de Ciencias de la Computación e Inteligencia Artificial, Universidad del País Vasco, 1998.

• Master management
• Where is the ETSIAAB
• OBJ./COMPETENCES
• Access requirements and applicant profile
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• Master thesis

In Computational Biology between ETSIAAB and ETSII

En biología computacional entre etsiaab y etsii.

• Students will choose one of the external internships offered by the MUBC. In the event that no offer attracts them, or none of the selected ones have been assigned (several students can select the same proposal), the student may make a proposal, fitting it into one of the MUBC subjects.
• In general, students will carry out their Final Master's Project on the same subject in which they have carried out their curricular internships. Students who have not done external internships may request to take their TFM following what is stated in point 1.
• Students must make a TFM proposal that must be approved by CAMBIC.
• In the event that no offer attracts them, or none of the selected ones have been assigned (several students can select the same proposal), the student must make a proposal, framing it in one of the MUBC subjects and indicating up to three teachers of the same who can act as directors.

The co-supervision of the TFM by two Professors is possible, given that is has previously been requested providing an academic justification and approved by the CAMBIC.

Evaluation panel 

Administrative Process of the Thesis defense

• The participation of students in research structures (research groups, research projects or others) during the completion of their TFM will be valued.
• The average grade obtained by the student in the Master's Degree in Computational Biology will be assessed.
• The number of honors obtained by the student in master's subjects will be assessed.

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• MÁSTER - MUISE 2023-2024
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Doctoral thesis, list of phd thesis  , links of interest.

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WHERE ARE WE?

SEARCH YOUR SPECIALITY

ISOM is an institution devoted to research, development, and innovation as well as to technology transfer to the industry QUANTUM GROWTH GAN MOLECULAR BEAM GROWTH MAGNETIC OPTICAL EPITAXY LAYERS CRECIMIENTO MOLECULARES EPITAXIA HACES MAGNETICO OPTICO CAPAS ISOM UPM SEMICONDUCTOR PLASMONICS SEMICONDUCTORES GAAS

Phd research at isom.

We are always looking for motivated and talented PhD candidates to join our team. ISOM provides a top-notch research environment for PhD candidates to pursue and achieve their scientific and technological goals in the areas of micro and nanoelectronics, optoelectronics, and photonics.

Available vacancies

Advanced packaging of gan-based hemts.

• Opening: 09/05/2024  |  Call
• Deadline: 31/05/2024

A funded PhD position for a outstanding candidate willing to pursue their PhD on the packaging of GaN HEMTs Contact: Jorge Pedrós Ayala

High-performance GaN-based HEMTs

Funded PhD position for an excellent candidate willing to pursue their PhD on GaN High Electron Mobility Transistors Contact: Jorge Pedrós Ayala

Development of novel Telecom single photon sources

• Opening: 24/11/2023  |  Call
• Deadline: 31/01/2024

Position for a highly motivated PhD student to contribute to a breakthrough project funded by Spanish Ministry of Science and Innovation on the subject of quantum communications. Contact: Jose María Ulloa

Magnetic switching in nanostructures under the influence of acoustic, thermal and surface interactions

• Opening: 06/10/2023  |  Call
• Deadline: 15/09/2023

Contact: José Luis Prieto Martín

Materiales Disruptivos Bidimensionales (MAD2D-CM)-UPM1

• Opening: 04/10/2023  |  Call
• Deadline: 07/06/2023

Contact: Jorge Pedrós Ayala

Contact: Alberto Boscá Mojena

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• Course schedule 2022-23
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Master Thesis

• Information for professors
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• Master of Technological Innovation in Health
• Email: master.ib@gbt.tfo.upm.es
• MÁSTER UNIVERSITARIO EN INGENIERÍA BIOMÉDICA

Master Thesis Defenses (2024 calls)

• DATES-DEFENSE-TFM JANUARY 2024

Documentation

• Master’s Thesis Regulations (Approved by the School Board 06-28-2019)
• Request for modifications of the TFM
• Request for cancellation of the TFM
• Guide for reflection on ethical, economic, social and environmental aspects [Date of publication: 05-24-2018]

TFM document template

TFM template with annexes [Date of publication: 06-11-2018]

TFM registration

Application for the TFM registration

Below are websites where you can find offers for Final Master’s Projects:

• Photonics and Bioengineering Technology (TFB)
• Electronic Engineering (DIE)
• Biomedical Technology Center
• Continuous Media Mechanics and Theory of Structures
• Materials Science
• Artificial intelligence
• Health and Human Performance
• Mechanical Engineering

Examples of TFMs from past courses

• Frequently-asked questions (FAQ)
• Contact information

• [email protected]

DOCTORADO INGENIERIA MECÁNICA

UPM PhD Mechanical Engineering

Presentation.

UPM’s Ph.D. (Doctor of Philosophy) Programme in Mechanical Engineering pursues the training of high-level researchers and professionals in all fields of Mechanical engineering and contributes to UPM’s strategy of excellence. Through a thesis-based approach, the Programme offers students a unique opportunity to work at the highest level of research, integrated in advanced research projects and teams, under the supervision of a faculty member. The results obtained so far have helped to bring UPM to the top-50 worldwide in Mechanical Engineering, according to the QS Ranking by Subjects (2019 and 2020).

These studies provide a very focused training in a selected field of Mechanical Engineering through a personalized hands-on approach aimed at the development of research and professional skills, depending on students’ background, experience and interests. Ph.D. students perform a singular research study, which should reach scientific excellence and demonstrate an impact availed by established quality criteria. The researchers should be able to work autonomously, but also in collaboration with colleagues from the different research groups of the department and are guided by expert researchers.

Ideally, the Ph.D. thesis will be developed in a period of three academic years, although special situations may lead to granting an extension of up to two additional years. The formative period culminates with the public defense of the Ph.D. thesis.

The Programme is delivered at UPM’s ETSI Industriales (Industrial Engineering School) with the involvement of several research groups, institutes and teaching units, mainly from the Mechanical Engineering Department. Prof. Jesús Félez is the current coordinator.

Current employability of Programme’s alumni reaches a value of 100%, with all our alumni working on R&D tasks directly related to their Ph.D. theses in the field of Mechanical Engineering (60% in academia and research centers, 40% in companies, see graph below).

The internationalisation degree of the Programme is remarkable, with around one international mobility per student and with c.a. 50% of Ph.D. Theses receiving the “international mention”. Professors of the Programme actively collaborate in funded R&D projects with researchers from world-class universities and research centres like: MIT, California Institute of Technology, Berkeley University, Georgia Tech, Technical University of Munich, Karlsruhe Institute of Technology, Technical University of Vienna, Politecnico di Milano, Politecnico di Torino, University of Pisa, Chalmers University of Technology, Tokio Tech, University of Leuven, Fuzhou University, Central South University, Tsching Hua University, to cite some examples.

The Ph.D. Programme is currently under evaluation (accreditation process by Madri+d Foundation) according to Spanish regulation RD 99/2011.

For information and advice, please feel free to contact directly Prof. Andrés Díaz Lantada, Ph.D. Programme’s Secretary (email: [email protected] ).

Research Lines

The Programme is structured across 6 main research lines:

• Machines engineering and product development.
• Manufacturing engineering.
• Computational dynamics and seismic engineering.
• Acoustic engineering.
• Vehicles and transports engineering.
• Laser engineering and applications.

These R&D lines cover the vast majority of research, development and innovation topics of modern Mechanical Engineering. Professors of the programme work mainly at UPM’s Mechanical Engineering Department (ETSI Industriales), and there is also a relevant involvement of professors from UPM’s Applied Physics and Materials Department (laser engineering and applications) and from ETSI Telecomunicación -Telecommunications School- (acoustic engineering).  

The following R&D institutes, centres and groups contribute to the scientific-technological excellence of the programme:

• UPM’s Centre for Railway Technologies (CITEF).
• UPM’s Centre for Automotive Research (INSIA).
• UPM’s Laser Centre.
• UPM’s Research Group in Machines Engineering.
• UPM’s Research Group in Manufacturing Engineering.
• UPM’s Research Group in Seismic Engineering.
• UPM’s Research Group in Applied Acoustics.

Different laboratories from Madrid’s “Red-Lab”, like UPM’s Product Development Lab, UPM’s Machines and Mechanisms Lab, UPM’s Materials Strength Lab and UPM’s Manufacturing Technologies Lab, support researchers during their Ph.D. studies. UPM’s International Doctorate School contributes to researchers’ multidisciplinary training.

Specific topics of research, grouped by R&D lines, are listed below as examples for prospective applicants.

Acoustic Engineering:

• Applied Acoustics
• Industrial Noise
• Environmental Noise
• Noise in Working Environments
• Architectural Acoustics
• Instrumentation
• Electroacoustics
• Acoustic Metrology
• Virtual Instrumentation
• Noise and Acoustic Signals Processing
• Neuroacoustics

Machines Engineering and Product Development

• Machines Engineering and Product Design
• Engineering Design Methods
• Design and Manufacturing with Advanced Materials
• Rapid Prototyping and Advanced Additive Technologies
• Machine Safety and Maintenance
• Vibroacoustics in Machines Engineering
• Artificial Intelligence for Mechanical Engineering
• Tribology and Surface Engineering
• History of Machines and Mechanisms
• Biomechanics and Bioengineering Design
• Tissue Engineering and Biofabrication
• Living Machines and Living Materials

Manufacturing Engineering

• Analysis, development and automation of processes
• Microfabrication
• Polymers Transformation
• Applied Robotics
• Additive Manufacturing
• Adaptative Machining Control
• Knowledge-Based Engineering for Design and Manufacturing
• Concurrent Engineering Modeling
• Design of Advanced Tools and Molds
• Product Lifecycle Management Systems
• Lean Manufacturing
• Discrete Systems Simulation for Enhanced Efficiency
• Modeling Manufacturing Systems
• Internet of Things and Cyberphysical Systems

Vehicles and Transports Engineering:

• Transport Safety
• Protection Systems for Vehicles
• Accidents Research: Causes and Consequences
• Impact Biomechanics for Passive Safety
• Impact Prediction in Vehicles and Transport
• Conduction-Aided Systems
• Improved Propulsion for Minimal Eco-impacts
• Computational Modeling Applied to Vehicles
• Dynamic Performance Enhancement for Vehicles

Railway Engineering:

• Virtual Reality
• CAD Systems
• Variational Geometry
• Multimedia Systems
• Modeling and Simulation
• Geographical Information Systems
• Computer-aided Engineering and Education
• Sensorisation
• Railways and Platforms
• Electrification, Substations, Catenaries
• Railway Safety and Signalling
• Communication in Transport Networks
• Vehicle Dynamics

Laser Technologies:

• Basic Laser-Matter Research
• High Intensity and Short Duration Laser Pulses and Matter
• Laser-Enabled Plasmas
• Industrial Applications for Laser Technologies
• Laser-Based Soldering
• Laser-Based Surface Treatments and Coatings
• Ultra-High Intensity Lasers and Applications
• Laser-Generated Shock Waves for Surface Treatments
• Laser Micromachining and Microforming
• Development of Laser Processes for Materials and Photovoltaics
• Laser-Based Applications for Optics, Micro-/Nano-Photonics and Fluidics
• Microfabrication of Photonic and Biophotonic Devices
• Optical Characterization of Photonic and Biophotonic Devices
• Advanced Optical Characterization Techniques
• Lasers for Environmental Applications
• Lasers for Biomedical Applications and Biofabrication

For detailed information, please feel free to contact the Programme Coordinator (Prof. Jesús Félez: [email protected] ) or Secretary (Prof. Andrés Díaz: [email protected] )

Access and Admission Criteria

Prospective Ph.D. students should fulfil one of the following access criteria according to RD 99/2011:

• Hold a Spanish Official Bachelor’s and Master’s Degree.
• Hold a Higher Education Degree from the European Area of Higher Education of at least 300 credits according to the European Credit Transfer System (ECTS), from which at least 60 should be at Master’s level.
• Hold a Spanish Official Bachelor’s Degree of at least 300 ECTS and necessarily take part in educational complements focused on research, if not previously studied.
• Hold another foreign Higher Education Degree, with an educational level equivalent to Spanish Master’s Degree and granting access to Ph.D. studies in the country of origin.
• Hold a previous Ph.D. degree.

On a yearly basis, UPM’s Ph.D. Programme in Mechanical Engineering offers 25 new positions for students worldwide. Students with background connected to the lines of research of the Ph.D. Programme (preferentially Mechanical Engineering, Industrial Engineering or directly related profiles) will be assessed on the basis of academic record, professional and research experience, English level, publications, motivation and recommendation letters and eventual interview. Educational complements shall apply to students without a clear Mechanical Engineering background.

Enrolment applications are managed through UPM’s Helios platform:

https://www.upm.es/helios/?idioma=E

For additional details or questions, please please feel free to contact the Programme Coordinator (Prof. Jesús Félez: [email protected] ) or Secretary (Prof. Andrés Díaz: [email protected] )

Ph.D. Theses: Quality Criteria

During the research period, Ph.D. students shall develop a Ph.D. Thesis in any of the Educational Units and Research Groups or Institutes already mentioned, or in any other institution (including companies through Industrial Ph.D. agreements), in agreement with the Programme’s Doctoral Commission (CAPD). The theses should be completed in three years, in the case of full-time dedication, plus eventual ordinary and extraordinary extensions (one-year each).

The Ph.D. Thesis is an original research work carried out in any area of modern Mechanical Engineering or clearly related to the fundamental research lines of the Programme.

The CAPD shall evaluate the research performed and eventually allow the public defence of the Ph.D. Thesis, once the quality and dissemination requisites of the Programme are fulfilled. These requisites foster academic and research excellence. The results from the Ph.D. theses should be published in international journals indexed in the “Journal Citation Reports” (JCR). In general, the CAPD will grant permission, which should be ratified by UPM’s Doctoral Commission, when at least one point is obtained according to the following criteria:

– Publication in JCR-indexed journal (Q1): 1 point.

– Publication in JCR-indexed journal (Q2): 0.75 points.

– Publication in JCR-indexed journal (Q3): 0.5 points.

– Publication in JCR-indexed journal (Q4): 0.25 points.

Once the quality criteria are verified and the Ph.D. research and Thesis document is finalized, public defence will be carried out in front of a Jury of experts in the field (5 members with a maximum of 2 members from the same university), elected and named according to the current legislation.

Additional details can be obtained by consulting UPM’s regulations and standards:

https://www.upm.es/Estudiantes/Estudios_Titulaciones/Estudios_Doctorado

Ir al contenido

• Grado en Ingeniería Informática
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Master's Degree in Digital Innovation

Second year students at upm (exit), general information.

The Master Thesis consists on the individual execution of an integral project, in which the competencies acquired during the master are demonstrated. The Master Thesis should consist of a written dissertation completed by the Master's degree candidate. The thesis document should have a clear structure and properly describe, justify and present the work done. The Master Thesis supervisor shall guide the student to ensure the right contents and format of the work. The Master Thesis document must be written and defended in English. Students will choose a thesis topic and submit the thesis proposal describing the work to be done. The name and the web of the company/institution and its main business must be presented in one paragraph. The Master Coordinator will approve and assign a Master Thesis Supervisor. The Coordinator may ask for further details on the proposal.

The completion of the Master Thesis is a three-phase process:

• Phase 1: Choice of topic and supervisor
• Phase 2: Completion of Master Thesis
• Phase 3: Defense of Master Thesis

The main tasks to be completed in each of these phases are briefly outlined below. Phase 1: Choice of topic and assignment of supervisor Master Thesis shall be supervised by a professor (UPM academic staff), from the Master's programme, with co-supervision by another Professor. Students must submit in the Moodle of the course a short thesis proposal (1-2 pages containing thesis objective/s, planning, methodology and expected results). Phase 2: Completion of Master Thesis The Master Thesis shall be supervised by a professor (UPM academic staff) from the Master's programme. Minor restrictions have been imposed regarding the format and lenght of the Master Thesis, being the Master Thesis director whom shall stablish them depending on the nature and contents of the work. Nevertheless, it is important to respect the following considerations:

• be written in English.
• page size shall be A4.
• the document must use the provided template.
• a minimum of 50 pages is requested

Phase 3: Defense of Master Thesis UPM Students shall defend their Master Thesis at the date established at the examination timetable. Students shall defend their Master Thesis individually in a public oral hearing before a board appointed by the Coordinator of the programme. The Board shall follow the following instructions, which rule the protocol for defending the Master Thesis:

• Once the Board designed by the Coordinator of the programme has been constituted, its Secretary shall open the public session and shall call the first canditate.
• The Secretary of the Board shall deliver among the other two Board members the Rubric-Based Assessment Document for Master's Final Thesis, also provided to him/her. They shall evaluate the Master Thesis guided by that document.
• Once the candidate is ready, the Board President shall announce the beginning of the defense, reading the title of the work, the name of the author and its directors.
• The student shall deliver a 20 minutes oral presentation, mainly focused on showing the nature and results of the work to the Board.
• After the student presentation, the Board President shall give way to the Board question turn. Each Board member shall be able to raise any question, query, clarification or objection that they like, to which the student shall have to give proper and convincing responses.
• Once the Board considers the question turn over, the student shall be invited to leave and each Board member shall individually complete the rubric-based assessment document, shall sign it and shall hand it back to the Board Secretary, which shall guard it from that moment on.
• Next, the Board Secretary shall call the next candidate, if there would be any, following the same procedure like with the previous candidate.
• When the last Thesis defense is finished, the Board President shall consider finished the public session and shall inform that the evaluation results will be published in the next days.
• The Board Secretary shall collect the signatures of the Board members in the Document of Defense of the Master Thesis, together with the observations the Board shall consider opportune to transmit to the Academic Commission.
• The Board Secretary shall deliver to the Master Secretary all the rubric-based assessment documents produced and the Document of defense of the Master Thesis of the session.

After receiving all the evaluations from the directors, co-directors and Board members for each Master Thesis defended in the same examination session, the final grades shall be published. From this point on, the same procedure as for any other course shall be followed.

FIRST YEAR STUDENTS AT UPM (ENTRY)

Although the student will defend the Master Thesis publicy according to the EXIT University procedure, they have to accomplish as well some UPM requirements. A Master Thesis shall be written sumarizing the main goals and results of the work carried out. All students must met UPM requirements to gain our official Master Programme.

Minor restrictions have been imposed regarding the format and lenght of the Master Thesis, being the Master Thesis director whom shall stablish them depending on the nature and contents of the work. Nevertheless, it is important to respect the following considerations:

Please find below a guideline about the main tasks to be accomplished: Task 1 Students must submit a short thesis proposal (1-2 pages containing thesis objective/s, planning, methodology and expected results).  After receiving all proposals, the student will be assigned a UPM thesis supervisor. This supervisor will only review the work done but final decisions on your work will be made by your EXIT supervisor. Task 2 Once the student has defended and submitted the master tesis work at the exit university and once the work is revised by the UPM supervisor, the student must submit the following documents:

• Master Thesis document in pdf format using UPM Master Thesis Template available in the Moodle of the course. Documents without UPM format won't be accepted.
• Confidentiality/no-confidentiality agreement , signed by the student and the UPM supervisor.
• Letter of authorization (optional) . In order to increase the visibility of the student work, we strongly encourage its publication in the UPM Digital Library. The student keeps its authoring rights on the work and it can be referenced and accessed from the web. So, if the student want to authorize the publication of the work, they must submit as well the signed "letter of authorization".
• Evaluation Report , signed and stamped by the academic coordinator at the exit university

Competences and learning outcomes

Competences

• CB08:To know how to evaluate and select the proper scientific theory and the precise methodology of their fields of study, and be able to integrate knowledge, to identify the consequences and make judgments based on incomplete or limited information  including, where necessary and relevant, a reflection on the social, ethical, and intergenerational responsibility linked to the solution proposed in each case by applying their knowledge and judgments.
• CB09:To know how to convey in a clear and unambiguous way to a specialized or not audience, results and conclusions from scientific and technological research and or from the most advanced innovation field, as well as the fundamentals more relevant and latest reasons supporting them.
• CG1: To be able to predict and control the evolution of complex situations through the development of new and innovative methodologies of work adapted to the particular scientific/research, technological or professional area, mostly multidisciplinary, in which the activity is developed. CEIT4 is covered in I & E
• CG2: To develop sufficient autonomy to participate in research projects and scientific or technological collaborations within their thematic scope exploring and generating new ideas systematically in interdisciplinary contexts and, in such a case, with a high component of knowledge transfer.
• CG3: The ability to use the English language in a competent way, i.e., with training for work and study complex tasks.
• CG5: To understand the principles of the project management, risk and change, as well as have the ability to apply methodologies and processes to manage projects and mitigate risks.
• CG6: To be able to manage information.

Learning outcomes

After taking the subject the students will be able to:

• Propose a justified solution to a real problem in a business working environment, giving answer to the wishes and needs of a consumer or customer in the frame of what technology can offer,  either complex or ill-defined, belonging to a new or emerging area, requiring the development of new and original approaches or methods, and/or multidisciplinary, justified in a qualitative and quantitative manner.
• Materialize the proposed solution to a problem in terms of code, prototype, reports, proof of concept, analysis, design and/or documentation, framing it into a real business environment.
• Apply knowledge, techniques, models and tools for innovation and entrepreneurship management to the transformation of an innovation into a viable commercial solution.
• Present and defend the proposed solution to a given problem in a clear manner without ambiguities in front of both a specialized and non-specialized audience.
• ETS de Ingenieros Informáticos
• - Universidad Politécnica de Madrid
• Directorio de Personal
• | Guía de navegación
• Última modificación: 17 de octubre de 2023

• Accesibilidad

Instituto de energía solar

• Únete al IES-UPM
• / PhD Thesis
• 01. PhD Thesis
• 02. Master Thesis
• 03. Other possitions offered

Positions offered

Phd thesis offer.

Departamento de Física y Estructuras de Edificación

In progress PhD thesis

Completed PhD thesis

• ← Impulso Verde Building
• Research Topics →

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Universidad Politécnica de Madrid on Overleaf

Quick start.

Universidad Politécnica de Madrid in the framework of the Digital Transformation Plan is providing Overleaf Professional features for all professors and researchers who would like to use a collaborative, online LaTeX editor for their projects. Overleaf Professional features include real-time track changes,unlimited collaborators, and full document history.

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Introducing Students to LaTeX

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