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on the biology and the regulation of protein lipidation. You uncover the role of the writers and erasers through molecular biology techniques combined with proteomics mass spectrometry. PhD position 2 is focused
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modelling of materials and machine learning. Experience in atomistic modelling (molecular dynamics, density functional theory) and machine learning is important, as well as a strong interest in pursuing
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Engineering, Computational Physics, Materials Science or a related discipline is required, with experience in atomistic modelling of materials and machine learning. Experience in atomistic modelling (molecular
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regulation of protein lipidation. You uncover the role of the writers and erasers through molecular biology techniques combined with proteomics mass spectrometry. PhD position 2 is focused on the development
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approaches, you will establish accelerated Design-Build-Test-Learn cycles to continuously improve models via active learning and guide evolutionary trajectories toward promising but otherwise inaccessible
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medicine. As a PhD student, you will: Build computational models that integrate molecular and genetic data to study possible OA treatments. Develop bioinformatics pipelines, network-based approaches
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trends. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular properties of catalysts together with statistical methods to derive predictive models
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the gap between advanced machine learning and clinical application. Your tasks are: - Exploring molecular dynamics and proteomics methods to enhance variant interpretation, which may also include a role for
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results, further research is guided by trial and error with the goal of deriving intuitive trends. Data-driven approaches are attractive alternatives. Descriptors are used to characterize the molecular
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for the accurately delivering drugs. By combining microfluidics, human tissue models, and patient involvement, the project aims to make treatments for hearing loss safer, more effective, and more widely accessible