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rotating subfields of the Cluster and annual retreats of the doctoral researchers as well as welcome and networking events are part of the programme. HEiKA's offer contributes to the continuous deepening
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course is recommended, in order to enable students to fully participate in and profit from the network. Full-time / part-time full-time Programme duration 9 semesters or more Beginning Other Tuition fees
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: Required qualifications: Experience with data analysis and familiarity with statistical software (e.g., R, Stata) The applicant must be fluent in oral and written English, see documentation requirements
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a small multicultural research group with PhD students and postdocs of different nationalities. Thus, the group’s communication is in English. We aim to analyze the metabolic homeostasis
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Marie Sklodowska-Curie Action (MSCA) Doctoral Networks
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-performance chain; therefore, enabling stability and control of novel manufacturing processes as well as achieving desired properties within materials science and engineering. Use-cases will be defined within
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processing, or optimisation to turn heterogeneous knowledge (channel/network state, maps and topology, mobility, hardware constraints, and task-level KPIs) into reliable and efficient decisions. The work spans
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). The Schirmeier lab is a small multicultural research group with PhD students and postdocs of different nationalities. Thus, the group’s communication is in English. We aim to analyze the metabolic homeostasis
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characterization of glycoside hydrolases, and a postdoc working on computational modelling of the same enzymes. The PhD focuses on ligand-observed NMR analyses and other relevant methods to provide insight
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communication skills an early career researcher with publication record, presentation skills and commitment to research integrity equipped with excellent software skills and hands-on experience with deep learning