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Field
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transfer learning to transpose the recurrent neural network (RNN) model available for supercritical CO2 power cycles to other cycles. Since thermodynamic conditions vary greatly depending on the fluid and
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, employees, IT infrastructure, specialized training). Second, they may require the use of quantitative models, data analysis, and algorithms, but these applications must also safeguard the data privacy and non
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hybrid systems combining DES and conventional extractants with a view to achieving synergies in terms of efficiency. • Optimize operating conditions (ultrasound, microwave activation) to improve
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quantitative skills (e.g., R, stock assessment models, statistical analysis). • Proficiency in English and French (Tahitian a strong asset). Place of work: Umeå Forms for funding or employment: Employment 4
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candidate, you will investigate the learning capabilities of feed-forward and recurrent models of neural circuits with various degrees of biological plausibility, with a focus on: Transferability of
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implementation of modeling approaches and empirical analyses Publication and presentation of research results in relevant journals and at (international) conferences Supervision of student seminar papers and
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-based modelling, quantitative resilience assessment, and risk analysis to simulate and optimise resilience strategies. The framework will be tested and refined through pilot studies in collaboration with
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plasma physics and space weather forecasting. The RIB-Wind project aims to overcome key limitations in current heliospheric models, which typically rely on empirical assumptions to define inner boundary
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perturbations. Climate and human-induced disturbances may alter forest ecosystems dynamics, modifying eventually their resilience to rapidly changing conditions. Previous studies based on the temporal analysis
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, specifically battery prototypes. What you will do Multiscale modelling to better understand RFB behavior and identify optimal hierarchical shaped pore- and electrode-structure to encounter optimum electrolyte as