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Field
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, productivity, and overall operational stability of the process plants can be determined. This rigorous analysis will provide a clear picture of the flexibility potential and set the stage for implementing
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representations. In this project, you will substantially improve quantitative magnetic resonance imaging (MRI) image quality using deep learning approaches. Quantitative MRI allows healthcare providers
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to these questions will provide a new and much needed understanding of the controls of fluid-flow processes in the Cornubian batholith and their controls on the fluid mobilization of geothermal heat and metals
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. We invite applications from motivated candidates with passion and a strong interest in conducting research and innovation activities on Application of Ultrasounds in Biotechnological Processes
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process (process-specific design) (2) Optimization of the LPBF process parameters to achieve desired microstructure and material properties, (3) In-depth materials, and property testing and (4) Prototype
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doctoral student who has Competence and skills: -Strong foundation in computer vision (e.g., object detection, image segmentation) and NLP (e.g., text reasoning, language models). -Knowledge of generative AI
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methods to study dynamic cellular processes (live calcium imaging, pathological protein assembly, imaging of signal cascades) at spatial levels ranging from a single molecule to (entire organs of) model
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maximum of ONE student per project. This process will ensure an excellent fit of student to project and also an excellent strategic fit of the project within the faculty. Project titles: Bayesian methods
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of dynamic processes to address global challenges, from mitigating the impacts of natural hazards and sustaining our habitat amid global change to responsibly managing georesources. We are part of
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scientists, clinicians, and engineers to push the boundaries of brain imaging and contribute to insights that can improve healthcare practice. Technical PhD Candidate In this role, you will be responsible