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6 Mar 2026 Job Information Organisation/Company UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE Department Computer ingineering Research Field Engineering » Computer engineering Researcher Profile First
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with a background in Biotechnology/Cognitive Psychology and Mental Health, who can demonstrate interest and knowledge on the topics of Cognitive Conflict, Mental Effort, and Emotions and on the methods
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“HORIA HULUBEI” National Institute for Research and Development in Physics and Nuclear Engineering | Romania | 6 days ago
) announces a vacancy notice for an Doctoral research assistant position within the Project PCE 65 - „Magnetic resonance methods for new observation timescales in the detection of radiation dose-rate response
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biomass fraction. The tasks include: DES design; DES preparation – The DES will be prepared by the most conventional heating and stirring method. The selected compounds are weighted and mixed in the right
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emulator of sea ice dynamics, trained using high-fidelity numerical simulations, (ii) variational data assimilation methods, and (iii) a simplified representation of physical processes in the atmospheric
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Rigorous, organised and methodical Management and leadership skills Website for additional job details https://emploi.cnrs.fr/Offres/CDD/UMR7107-ANIFOR-019/Default.aspx Work Location(s) Number of offers
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will develop novel ligands for a validated drug target in acute myeloid leukemia. Building on our promising preliminary results, you will optimize and expand the hit discovery using computer-aided tools
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data, and apply the computational methods to understand biomedical problems like tumor microenvironment, immunology, and aging. The individual will work with Dr. Yi Zhang (https://yi-zhang-compbio
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shall have an estimated duration of 24 months, and should not be extended further than the project duration. The contract should begin on 1 st Maio 2026. Submission of Applications: Applications must be
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currently lack reliable uncertainty estimates, limiting error detection and automation. The UMLFF project aims to develop next-generation MLFFs with built-in uncertainty predictions to enable safe, automated