Sort by
Refine Your Search
-
Category
-
Program
-
Employer
- Barcelona Beta Brain Research Center
- Autonomous University of Madrid (Universidad Autónoma de Madrid)
- Basque Center for Macromolecular Design and Engineering POLYMAT Fundazioa
- CSIC
- FIIB HUIS-HUHEN
- Institut de Físiques d'Altes Energies (IFAE)
- UNIVERSIDAD POLITECNICA DE MADRID
- Universidad Politecnica de Cartagena
- Universidad Pontificia Comillas
-
Field
-
to the topic, including food safety, microbiology, computational biology, machine learning, artificial intelligence, data science, or other related scientific fields. Familiarity with data-driven
-
for the Rural Spain: Adaptation of Large Language Models (LLMs) and Speech Recognizers to Rural Speech" (SI4/PJI/2024-00237), granted in the 2024 call for R&D Project Grants for Emerging PhDs by the Universidad
-
architectures for TTS and ASR Entrenamiento de modelos a gran escala utilizando frameworks modernos de deep learning / Training large-scale models using modern deep learning frameworks Publicaciones en
-
infrastructure (e.g. Observatorio del Roque de los Muchachos) Hands-on training in cutting-edge techniques, from detector R&D to advanced data analysis and machine learning. Attendance to international
-
of Spanish (not required but valued for teaching and policy dissemination in Spain). Experience with AI-based research workflows, machine learning techniques applied to financial data, or modern
-
). Familiarity with machine learning techniques, particularly LSTMs or other deep learning architectures. Experience with large datasets, geospatial analysis, or database development. Knowledge of ecological flows
-
predictive modelling; Bioinformatics and Knowledge Graphs (visualization and reporting); AI-based data integration across cohorts (with federated machine learning); Contribute to ongoing projects, such as: o
-
the production of polymer latexes that involves a complex, heterogeneous polymerization system and leads to polymers with a diverse range of structures. This project looks to use machine learning to better target
-
conference presentations. - Knowledge of machine learning applied to clinical data. - Ability to communicate technical results to both specialized and non-specialized audiences. - Knowledge of advanced
-
, or a related field. Proven experience in machine learning, deep learning, generative AI and data mining. Strong programming skills (e.g., Python, R, MATLAB, or similar). Experience with data