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their decisions and businesses in their strategies. Do you want to know more about LIST? Check our website: https://www.list.lu/ How will you contribute? The position is in the NMR facility within LIST’s Elemental
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(industry, grid operators, regulators) and an interest in societal impact pathways As a formal qualification, you must hold a PhD degree (or equivalent). You will be assessed against the responsibilities and
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the Human Resources area at https://www.ua.pt/en/sgrh/pessoal-investigador-novos-concursos-e-ofertas . 2.4.3 — When formalizing the application, if a classified document is submitted that reveals a commercial
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-Doctoral Research Fellowships to hold a PhD degree obtained in the 3 years previously to the submission of the fellowship application; to have carried out the research work that led to the PhD degree in a
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written English, ability to work both independently and collaboratively. Additional qualifications Experience or coursework in one or more of the following areas is considered an advantage: formal methods
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CIIMAR - Interdisciplinary Center of Marine and Environmental Research - Uporto | Portugal | 14 days ago
First Stage Researcher (R1) Positions PhD Positions Application Deadline 27 Mar 2026 - 23:59 (Europe/Lisbon) Country Portugal Type of Contract Not Applicable Job Status Full-time Hours Per Week 40 Offer
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the formal and functional properties of gestures and their relationship to discourse. - Support for computational modeling: o Contributing to the gesture–speech modeling carried out by Loria, based
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, United States of America [map ] Subject Area: Formal Methods and Machine Learning for Mathematics Appl Deadline: none (posted 2025/10/03 05:00 AM UnitedKingdomTime) Position Description: Apply Position Description
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research and education in systems analysis, modelling and simulation, intelligent information systems, and software-intensive systems engineering. The Institute plays a key role in advancing data-driven
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, and datasets; often at substantial computational and environmental costs. This PhD project targets sustainable and resource-efficient machine learning with a focus on methods that reduce compute, energy