Daniel Hernández Lobato
Closing soon
5 days ago
PhD in Machine Learning for Human-Aware and Efficient Machine Learning (HEMLA) at Universidad Autónoma de Madrid Universidad Autónoma de Madrid in Spain
Degree Level
PhD
Field of study
Computer Science
Funding
Fully funded four-year FPI PhD position within the HEMLA project. The post mentions access to state-of-the-art computing infrastructure and opportunities for international research stays, collaborations, conferences and workshops.
Deadline
Oct 18, 2026
Country
Spain
University
Universidad Autónoma de Madrid

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About this position
PhD opportunity in Machine Learning at Universidad Autónoma de Madrid (UAM), within the Human-Aware and Efficient Machine Learning (HEMLA) project and the Machine Learning Group (GAA).
The position is a fully funded four-year FPI PhD focused on developing machine learning methods that are efficient, trustworthy, interpretable, and aligned with human preferences. Research topics include explainable AI, Bayesian deep learning, uncertainty quantification, scalable kernel methods, and functional data analysis, with applications in healthcare and renewable energy forecasting.
Applicants should have a Master's degree or equivalent in a relevant field and a strong background in machine learning, optimisation, probability, or statistics. Programming skills are expected, preferably in Python and PyTorch.
The post highlights access to state-of-the-art computing infrastructure and opportunities for international research stays, collaborations, conferences, and workshops.
To apply, email [email protected] with your CV and academic transcripts before 18 October 2026.
Funding details
Fully funded four-year FPI PhD position within the HEMLA project. The post mentions access to state-of-the-art computing infrastructure and opportunities for international research stays, collaborations, conferences and workshops.
What's required
Applicants should hold a Master's degree or equivalent in a relevant field. A strong background in machine learning, optimisation, probability or statistics is required, and candidates should be comfortable programming, preferably in Python and PyTorch.
How to apply
Email Daniel Hernández Lobato at [email protected] with your CV and academic transcripts before 18 October 2026.
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