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Postdoctoral researcher in marine ecosystems modelling for the Marine and Continental Waters Program
involving nutrients, pollutants, microalgal populations of planktonic and benthic ecosystems. Experience on oceanographic studies, involving population dynamics studies and trophic web interactions. Knowledge
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to apply E-mail nair.lopez@uam.es Requirements Research FieldPhysicsEducation LevelPhD or equivalent Skills/Qualifications PhD in Physics, material science or equivalent Previous experience/knowledge in
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or equivalent Skills/Qualifications PhD completed Between Jan 2023-Dec 2025 Knowledge of nutrition, paediatrics, physical activity and endocrinology. Experience in managing data from cohorts. Experience in
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of the thesis and published articles: 25 • Experience and Participation in Euclid and relevant international projects: 25 • Motivation: 10 • Prior knowledge directly related to the project: 40 Selection process
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and December 31, 2025 (within the two years prior to the upcoming call). Is language knowledge required for this position? Yes. Although it is not a formal requirement of the call, a good level of
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knowledge of supramolecular techniques (calorimetry) and X-ray diffraction related to the project. Evaluation of the catalytic properties of the synthesized compounds. - Writing reports and articles
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position requirements (skills/languages/required experience) Studies Doctorate in Geography, Economics, Political Science, Sociology or related disciplines Computer Science • Advanced knowledge
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experience is a plus Strong engineering background, with previous experience in: Finite Element modelling Agent-based modelling Network modelling (systems biology) knowledge of statistics is a plus Additional
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Inverse problems and statistical signal processing Knowledge in optical sensing methods LanguagesENGLISHLevelExcellent LanguagesSPANISHLevelGood Research FieldEngineering » Communication engineeringYears
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, Computational Environmental Sciences, or a related field. Essential Knowledge and Professional Experience Familiarity with foundation models, adaptation techniques, and fine-tuning (model development in PyTorch