In the Centre of Industrial Electronics and Multimodal Systems (CEIMM) at Universidad Politécnica de Madrid (UPM), we are looking for a highly motivated and talented PhD student in the field of Affective Computing and embedded AI for physiological signals. The proposed PhD research project is focused on the following topics:
- Physiological Signal Processing (ECG, EDA, PPG, motion, voice)
- Tiny Recursive Models (TRMs) for on-device detection of affective states and physiological anomalies
- Foundation Models for physiological signals: self-supervised pretraining, personalisation, distillation and continual recalibration
- Embedded Machine Learning on RISC-V platforms
- People-Centric Internet of Things with privacy by design and explainable feedback
The thesis builds on a decade of work at CEIMM in affective computing, from classical machine learning to embedded deep learning, and takes the next technological step: tiny recursive models running at milliwatt budgets on wearable devices, backed by physiological foundation models pretrained on large unlabelled multimodal corpora and fine-tuned per person and per job.
BASIC QUALIFICATIONS: Master’s degree in Electrical / Electronics / Computer Engineering, Computer Science, Physics or similar. Previous research activities in the fields of interest will be highly appreciated.
PREFERRED SKILLS: Very good knowledge of Programming in C/C++ and/or Python; Signal and Image Processing; experience with Machine Learning and/or Affective Computing is also valuable. A high level of English is required, Spanish is optional.
POSITION AVAILABLE: The selection procedure will open in October 2026 and will be maintained opened until the position is covered. The starting date is flexible, but preferred ASAP.
INFORMATION: If you are interested in this position and want to have more details about it, please contact Dr. Andrés Otero ([email protected]), including the reference [PhD_Affective] in the subject of your email. For the formal application, the following documents will be required:
- CV
- Academic transcript of courses followed, and grades obtained provided by your institution.
- Brief letter describing your motivation and previous experience.
