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challenging, and new theoretical methods and algorithms are required. The research project aims at deriving priors for Bayesian methods from atomistic simulations and machine learning. It also offers
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organisms, in order to maximize terpene production of transgenic strains. To this end, genetic, molecular, physiological, and computational methods will be employed. The group is superbly equipped
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on methods development in machine learning, uncertainty quantification and high performance computing with context of applications from the natural sciences, engineering and beyond. It is embedded in
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located at the “Friedrich List” Faculty of Transport and Traffic Sciences. It is engaged in research and teaching in the four research fields of Mobility Behavior, Survey Methods, Traffic Safety, as
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of the following elements: Personal data and funds requested Outline of proposal (detailed requirements as to content and page length can be found on the website) Request to invite a Visiting Fellow or experts
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PhD project: Protein-protein interactions (PPIs) mediate most cellular processes and are key in understanding disease mechanisms. Various high throughput (HTP) methods map at increasing depth and
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and writing skills provided by the UKE Academy of Biomedical Health Sciences (ABHS)Specific courses in key skills, interdisciplinary topics, and research methods Weekly seminars and journal clubs
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-efficient neuromorphic computing inspired by the functioning of the human brain. Two-dimensional materials such as graphene and transition metal dichalcogenides (TMDCs) offer high potential for scaling and
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methods, including coarse-grained and atomistic molecular dynamics, systematic coarse-graining, machine learning, and continuum solvers for hydrodynamics, such as the lattice-Boltzmann method. Among other
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tomography) alongside conventional Evo-Devo and molecular biology methods.