PhD in Electronic Engineering

Academic year 2025-26

See the pre-registration, admission and registration deadlines for the 2025-26 academic year.
You may also check this information for the 2024-25 academic year.

Degree data and benchmarks

Below are the main data and benchmarks, as well as the results of the satisfaction surveys for the degree programme, which are periodically analysed and monitored by the centre administrator, the head of studies and the corresponding quality assurance commissions, with a view to promoting the ongoing improvement of the programme.

 

Satisfaction of groups involved

Satisfaction index

 
2019-20 2020-21 2021-22 2022-23 2023-24
Student satisfaction - - 3,50 - -
PDI satisfaction 3,67 3,25 - - -
PAS satisfaction 3,00 - - - 3,00

Description

Student, PDI and PAS satisfaction levels are rated on a scale of 1 to 4, where 1 means ‘not at all satisfied’ and 4 means ‘very satisfied’. They are obtained from the following surveys:

  • Survey of student satisfaction with the study programme (yearly)
  • Survey of satisfaction and identification of needs of teaching and research staff (PDI) (yearly)
  • Biennial survey of satisfaction and identification of needs of administrative and service staff (PAS) who have supported undergraduate and postgraduate studies.

 

Survey participation rates

 
2019-20 2020-21 2021-22 2022-23 2023-24
Student participation rate 22 % 30 % 57 % 25 % -
PDI participation rate 36 % 44 % 29 % 20 % -
PAS participation rate 50 % - - - 47 %

Description

Only the data that meet a double criterion of representativeness have been considered valid: on one hand, reaching 4 answered surveys and, on the other hand, that the number of answered surveys is equal to or greater than 15% of the population

Thesis

Link to the institutional repository (DSPACE)

Year Thesis
2023
  • Desarrollo de Técnicas Hardware No-Convencionales para su Aplicación al Aprendizaje Automático
  • On the use of stochastic logic for non-linear circuits and systems
2022
  • "Hardware Implementation of Machine Learning and Deep-Learning Systems oriented to Image Processing"
  • Compact Machine Learning Systems with Reconfigurable Computing
2021
  • CONTRIBUTIONS ON USING EMBEDDED MEMORY CIRCUITS AS PHYSICALLY UNCLONABLE FUNCTIONS CONSIDERING RELIABILITY ISSUES
  • Fully integrated CMOS-MEMS resonators as a biosensing platform
  • Instrumentación y caracterización del flujo térmico de materiales con aplicación al ahorro energético en edificación.
2019
  • "Modelling and Applications of Memristive Devices"
  • Developments for an embedded and reliable floating gate dosimeter
  • Nonlinear and chaotic behavior in CMOS-MEMS resonators
2018
  • A Contribution to the Characterization of Radiation-induced Soft Errors in Sigma-Delta Modulators and SRAM Memories
  • Desarrollo y aceleración hardware de metodologías de descripción y comparación de compuestos orgánicos
  • Shallow water remote sensing using sonar improved with geostatistics and stochastic resonance data processing