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Field
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for neuroimaging methodology, including: Designing, adapting, and overseeing multimodal MRI acquisition protocols (structural, diffusion, functional MRI), with attention to pediatric constraints Ensuring
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configurations hinders the creation of generalizable solutions for processing these images. This project proposes an innovative approach that combines state-of-the-art diffusion models with physical radar
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thin-film growth and surface chemistry. The successful candidates will focus on understanding and designing the ALD growth process at a fundamental level, while also investigating the optical properties
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these images. This project proposes an innovative approach that combines state-of-the-art diffusion models with physical radar knowledge and advanced transfer learning techniques. The methodology incorporates
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us to better predict their future survival, improving their local-global conservation policies. In this project, we will address this by combining high-resolution micro-Computed Tomography and AI
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, and deep generative models (e.g., VAEs, normalizing flows, diffusion models). Hands-on experience in multi- and hyperspectral image processing (e.g., IDL/ENVI) and RTM inversion (e.g., ARTMO
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layouts; quantify performance (energy/angular resolution, trigger efficiency, background rejection, sensitivity, trade-offs); Define a SWGO–CTAO synergy roadmap for key programs: transients, diffuse
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for 2 years The research group of Dr. Shahram Eivazi at the University of Tübingen is working on the Autonomous Design Systems and Embodied Intelligence, leveraging generative models such as diffusion
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for the Compton polarimeter. This activity will be complemented by experimental developments aiming to control the optical polarization with extreme precision, better than parts per thousand. Indeed, this is a
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students, STEM teachers, healthcare professionals) multimodal multichannel data (e.g., log files, eye-tracking, physiological sensors, facial expressions of emotions, screen recordings, etc.), using various