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development of prompt engineering, machine learning, and NLP techniques to process data and build this model, adding feedback based on the results. The selected candidate must live in Madrid during the period
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Postdoctoral researcher in marine ecosystems modelling for the Marine and Continental Waters Program
of machine learning and AI algorithms and methods. Knowledge of species distribution models. Catalan and Spanish are valued LanguagesENGLISHLevelGood Research FieldOtherYears of Research Experience1 - 4
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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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. Developing workflows and machine learning algorithms to accelerate catalyst design (optional). Group: Atomistic & Molecular Modelling for Catalysis Group Requirements Specific Requirements PhD in Chemistry
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experience, adequacy to the proposed profile (0-50 points): Strong background in wireless communications, mathematics, and machine learning, programming skills. Other merits (0-20 points): Fluent oral and
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the following objective included in this R&D&i Project “Knowledge Generation” PID2022-136435NB-I00, funded by MICIN/AEI/10.13039/ 501100011033 and by “ERDF A way of making Europe”, EU: - “O8. Speeding up Machine
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interpretable machine learning, System integration implementation, Test environment configuration, Validation and stress testing, Deployment and configuration in test environments, Component restructuring, review
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, computer science, bioengineering, data science, or a closely related discipline. • Demonstrate advanced proficiency in artificial intelligence and machine learning, particularly in applications involving
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or equivalent Skills/Qualifications Valued/Preferred qualifications: Experience in software solution development Experience with machine learning models Experience in R&D&I projects (Research, Development and
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transcriptomics data analysis. Experience in quantitative image analysis, computer vision, or digital pathology. A strong background in cancer biology or immunology. Experience with machine learning, deep learning