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developing genetics-based models for predicting future tree performance under changing climates. For more information about the departments, visit: Department of Forest Biomaterials and Technology | slu.se
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of Electrical Engineering. Project description: Autonomous vehicles for mining and mobile robotics for industrial processes require complex motion planning as well as safe model-based predictive control and
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penetrated many new application areas. Examples include control of autonomous vehicles based on video data, simulation-based prediction of turbulent flows and precision medicine based on gene sequencing time
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-resistant posture and physiology. The project aims to reconstruct, measure and predict the sensitivity and resilience of Swedish conifer forests to interacting soil fertility and droughts. Specifically, we
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slow decline to a steady-state viral load, or set point, of between 3-5 log/ml at approximately four months post-infection. Peak viral load levels in adults are not predictive of the rate of disease
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systems uses data to improve their own performance, understanding, and to make accurate predictions and has a close connection to applications. Project description This PhD project focuses on advancing
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learning methodologies to extract conformational ensembles from time-resolved X-ray solution scattering data of chemical reactions. The project focusses on transition metal photocatalysts and is in
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the understanding and predictive capability of multiphase systems, with applications in sustainable energy technologies. We are seeking applicants with: MSc in Mechanical/Thermal Engineering, Energy Systems, or a
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fluid dynamics and heat transfer to study multiphase flow phenomena. The goal is to integrate theoretical and experimental fluid dynamics with modern computational tools to analyze and predict multiphase
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safety. The goal of this PhD project is to increase knowledge of thermodynamic and material processes in marine hydrogen propulsion systems and to develop predictive models that ensure safe and efficient