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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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of transmembrane β-barrel nanopores. The group provides an interdisciplinary environment where computational method development is closely integrated with experimental validation and large dataset
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of transmembrane β-barrel nanopores. The group provides an interdisciplinary environment where computational method development is closely integrated with experimental validation and large dataset
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laude) based on your study results or professional realizations; Proficiency in oral and written English in accordance with the criteria of KU Leuven (more information available here ). A recently
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conformational landscapes and regulatory mechanisms Integration of structural and functional data to uncover novel regulatory interfaces Collaboration with leading academic and industrial partners across Europe
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sequencing data generation and interpretation Bioinformatics and statistical analysis, to characterize genome evolution, mutation processes, and adaptation patterns The PhD candidate will progressively
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sequencing data generation and interpretation Bioinformatics and statistical analysis, to characterize genome evolution, mutation processes, and adaptation patterns The PhD candidate will progressively
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). Please upload: Your curriculum vitae. A motivation letter detailing your research interests, experience and motivation to join the team. Contact information of 2 references. University degree transcripts