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Field
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This multidisciplinary position is part of a WASP NEST (Novelty, Excellence, Synergy, Teams) project focused on advancing generative models and perceptual understanding in computer vision. The
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collaborative mindset and bring the following qualifications: a PhD degree in mathematics, with a focus on algebraic geometry; experience with at least some of the following: Fourier—Mukai transforms, Hochschild
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the project include mathematical derivation, analysis, and comparison of models, methods, and simulation approaches; rapid prototyping of new ideas in custom code; implementation of new models, methods, and
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wastewater to tackle the environmental threat of AMR. You will have a PhD (or close to completion) in modelling complex systems or a closely allied discipline along with experience in creating, writing
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. We are seeking a Postdoctoral Researcher to join the team and make significant contributions to the field. The researcher is expected to have (i) strong machine learning skills to improve model
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Interdisciplinary Training in Mathematics for Human Health,” supported by the National Science Foundation. RTG themes include modeling in cancer biology, computational neuroscience, and mathematical epidemiology
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wastewater to tackle the environmental threat of AMR. You will have a PhD (or close to completion) in modelling complex systems or a closely allied discipline along with experience in creating, writing
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(Required) PhD in Information Theory, Mathematics or related fields Strong research skills and a solid track record of publications Excellent written and verbal communication skills in English. Knowledge
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analyzing whole organism models (zebrafish and mouse) to dissect the pathophysiology of a recently identified rare pediatric neurometabolic disorder. The approach involves mainly deep longitudinal phenotyping
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results into practical applications for end users. Subject Description The research aims to develop machine learning models for microbe detection, focusing on the mathematical foundations in geometry and