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Field
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& Collaboration The successful candidate will work at the interface of machine learning and biostatistics, developing new theory, algorithms, and scalable implementations. By establishing a new class of multi-frame
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The Quantitative Genetics research group is interested in developing statistical genomics toolboxes to decipher the genetic architecture of important crop traits, such as grain yield, adaptation
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experimental data are now available or it may use population-scale genetic, clinical, or public health data from pathogen surveillance efforts and biobanks. The future of life science is data driven. Will you be
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biology or host-microbe systems for which multidimensional, genome-scale experimental data are now available or it may use population-scale genetic, clinical, or public health data from pathogen
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on high-fidelity modelling and test data for both metals and thermo-set composite materials. To achieve this we will explore the use of advanced genetic algorithms and/or Artificial Intelligence (AI
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molecular biology techniques as well as in algorithms, statistics and artificial intelligence for molecular genetics. Importantly, mastery of the experimental and theoretical aspects shall equip doctoral
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scientists of the University, University Hospital, and Max Planck Institute Münster as well as of the RWTH Aachen. Our central objective is to elucidate the genetic, molecular, and cellular mechanisms
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experimental molecular biology and data analysis. Doctoral candidates can specialize in genomic and molecular biology techniques, as well as in algorithms, statistics, and artificial intelligence for molecular
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Evolutionary Bias Database for Nucleic Acid Structures (NA3D4U) Built with Help of AI Development and integration of microbial biomass database with AI supported collection and validation of data Genetic-Code
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Genetics (GMG) initiative in collaboration with the Global Methane Hub and the Bezos Earth Fund. The global program with more than 50 partners across 25 countries aims to accelerate genetic progress in