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Field
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, automated reasoning, constraint or answer-set programming (ASP), rule-based systems); knowledge representation (ontologies, knowledge graphs, state machines); formal methods and verification (model checking
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. Knowledge of applications of quantum computers to many-body physics problems (e.g., quantum simulation algorithms, hybrid quantum optimisation methods). Other Valued Skills Experience in supervising Master or
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team, Aarhus University and MAX IV. Depending on the candidate’s interests and expertise, research activities may include studies of functional materials, advanced crystallographic methods, automated and
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of the doctorate degree, under the applicable legislation. This formality must be fulfilled up to the date of signing the contract. 2 — Formalization of the applications: 2.1 — The Applications must be accompanied
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evolutionary genomics and population genetics, with demonstrated experience applying evolutionary-genomic methods to whole-genome data. Extensive hands-on experience with bioinformatic analysis of whole-genome
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instruments, with data obtained with DNA methods and data obtained with a Hirst trap. • Numerical models used by the group covers the particle dispersion model HYSPLIT in combination and the numerical models
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candidate will have an existing research profile that uses ethnographic methods to explore questions relevant to the social and ecological effects of livestock and agricultural infrastructures on landscapes
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genetic basis of plant–microbe interactions, with a particular emphasis on data integration across plant species and data types (genomics, transcriptomics). Design, adapt and use deep learning methods
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and work in the interface of basic cellular biology /virology and experimental clinical medicine trials meeting the challenges of developing methods to the highest international scientific standards As
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: Conceptualisation and synthesis of data integration and visualisation workflows. Data management and the development of knowledge graphs Development and application of AI and machine learning methods and pipelines