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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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. 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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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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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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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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, machine learning), to unveil the secrets of three post-transcriptional regulatory layers: the epitranscriptome, RNA structure and ribosome specialization. Whom would we like to hire? Professional experience
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» Computer engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country Spain Application Deadline 19 Sep 2025 - 23:59 (Europe/Madrid) Type of Contract Temporary Job Status Full
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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
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Leonardo. The successful candidate will play a crucial role in developing and optimizing machine learning workflows for large-scale environmental data analysis, contributing to the creation of robust and