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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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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
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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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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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methods in 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
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forward to receiving your application! We are looking for a PhD student in theoretical physics with a focus on AI-assisted workflows and database development for materials property prediction. Your work
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translational research topic that deals with radiation physics applied to medicine and biology for medical radiation imaging and radiation therapy. The advertised PhD position focus on assessment, prediction and
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application! We are looking for a PhD student in Medical Science. Your work assignments As a PhD student, you will participate in the project: Predictive markers for chemotherapy-induced toxicity in childhood
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bioinformatics pipelines for the metabolomics data analysis and visualization of metabolomics data, support the integration of software tools for data (pre-)processing, biomarker discovery, and predictive