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- UNIVERSIDAD POLITECNICA DE MADRID
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Field
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knowledge of Bioinformatics. Knowledge of Machine Learning applied to biological data. Experience in the use of Comparative Genomics software. Knowledge of the EukProt database. Proficiency in Python. Oral
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internal reports and manuscripts. Requirements: PhD in Physics, Materials Science, Computational Science/Engineering, Computer Science, or related. Solid knowledge of machine learning, including graph neural
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atmospheres and detectability studies Model development of 3D stellar atmospheres Applications of machine learning and AI to exoplanet data analysis Biomarkers and habitability of Earth-like planets Where
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, Biomedical Sciences, Engineering, Mathematics, Statistics, Physics, or related fields. PhD is welcome (plus), but not required. Essential Knowledge and Professional Experience Expertise in machine learning
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5 Sep 2025 Job Information Organisation/Company UNIVERSIDAD POLITECNICA DE MADRID Department HRS4R Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1
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title: Human-Centred Machine Learning: Lighter, Clearer, Safer (ACHILLES) Research line / Scientific-technical services: AI in health Grant/funding period: START: 01/11/2024 END: 31/10/2028 Centre
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computationally efficient algorithms. They will work with machine learning, deep learning, and NLP tools. Funding This contract is part of the GAP project (PID2022-139308OA-I00), funded by MCIN/AEI/10.13039
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systems for artificial intelligence and machine learning. - Programming very low power consumption embedded systems. - Cloud and edge computing. - Development of embedded systems for the Internet of Things
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pathways, including deactivation processes. Screening and fine-tuning catalysts to enhance performance. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group
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), técnicas y herramientas software de análisis de datos, machine/deep learning (Pandas, SHAP, TensorFlow, etc.) y específicas de análisis de imágenes, estadística, simulación, entornos cloud (tipo Kubernetes