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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | 23 days ago
execution of cognitive behavioral tasks; - Acquisition and analysis of neural signals obtained through either in vivo eletrophysiological or neuroimaging methods; - Computer programming for data analysis
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fast-paced environment. Knowledge of CPR and basic concept of abnormal vital sign parameters is a must. Candidate will also need strong organizational, prioritization, basic computer navigation skills
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environment. Knowledge of CPR and basic concept of abnormal vital sign parameters is a must. Candidate will also need strong organizational, prioritization, basic computer navigation skills, and a working
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vendors and affinity partners to: develop and outline contract parameters, establish event timelines and deadlines, select appropriate media channels, establish group sales performance goals and collaborate
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) Knowledge/Skills/Abilities Knowledge: A strong working knowledge of advanced epidemiologic theory and general predictive modeling methods, including model design, parameter estimation, sensitivity analysis
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enjoy working in a fast-paced environment. Knowledge of CPR and basic concept of abnormal vital sign parameters is a must. Candidate will also need strong organizational, prioritization, basic computer
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enjoy working in a fast-paced environment. Knowledge of CPR and basic concept of abnormal vital sign parameters is a must. Candidate will also need strong organizational, prioritization, basic computer
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fast paced environment. Knowledge of CPR and basic concept of abnormal vital sign parameters is a must. Candidate will also need strong organizational, prioritization, basic computer navigation skills
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in a finite element code. Study the different dynamic regimes depending on the model parameters. Where to apply Website https://seuelectronica.upc.edu/en/procedures/call-for-recruitment-of-pdi-postdo
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with physics-based models Developing robust and adaptive methods for real-time parameter and state estimation Implementing machine learning approaches that preserve physical constraints while handling