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in mathematics and topics related to machine learning. EVALUATION CRITERIA The selection will be based on the following criteria: Academic CV (60%) Previous experience in related projects (20
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Computer Science (or close field) with strong foundations in data management, machine learning, and software engineering. Coursework or projects in NLP/LLMs, information retrieval, knowledge graphs/ontologies, data
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the following conditions: OBJECTIVES | FUNCTIONS The purpose is to continue the research on Machine Learning methods applied to optimization techniques, in particular for the veicule routing problem. The idea is
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resulting precipitation and extreme weather. We study global and regional climate change and are at the core of international community climate modeling efforts that also involve AI and Machine Learning. We
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. In addition, you must have: a solid foundation in energy technology and a strong understanding of artificial intelligence (AI), machine learning (ML), and data-driven modeling documented experience
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economic assessments machine learning or proxy-model based methods field scale simulation geological features geomechanics reactive flow The PhD fellow are not expected to master all these topics. Project
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to: compositional multiphase reservoir simulation upscaling or screening methodologies optimization of well positions and control strategies economic assessments machine learning or proxy-model based methods field
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Profile First Stage Researcher (R1) Positions PhD Positions Country Norway Application Deadline 4 Jan 2026 - 23:59 (Europe/Oslo) Type of Contract Temporary Job Status Full-time Hours Per Week 37,5 Is the
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23 Dec 2025 Job Information Organisation/Company INESC ID Research Field Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions Master Positions Country Portugal
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Python for scientific computing – experience with data analysis and basic signal processing – foundations in machine learning and interest in developing advanced AI models – familiarity with Linux