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at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum
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materials, (d) Artificial Intelligence (AI) models to predict and control the construction process, (e) a digital twin / information backbone that enables cohesive operation of the design and production
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at the Department of Electrical and Computer Engineering, Aarhus University, where we are advancing communication-efficient and distributed foundation model inference across the computing continuum
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Postdoc in Decoding Biological Nitrification Inhibition (BNI) in Cereals: Integrating Metabolomic...
An ability to take initiative, develop, and manage research activities Proficient quantitative skills with data analysis and programming e.g. in R and python Documented experience in scientific writing and
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An ability to take initiative, develop, and manage research activities Proficient quantitative skills with data analysis and programming e.g. in R and python Documented experience in scientific writing and
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: Establish and develop experimental protocols and pipelines and implement data management compliance. Presentation of your work in various meetings (locally at the department, national and international
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. Key activities include analysis of clinical and patient-reported outcome data, coordination of implementation studies in collaboration with ENT clinics, dissemination of results through peer-reviewed
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: Establish and develop experimental protocols and pipelines and implement data management compliance. Presentation of your work in various meetings (locally at the department, national and international
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diagnostic pathways. Core research tasks include planning, conducting and publishing epidemiological studies using large-scale observational data, primarily register-based, with a focus on effects of ADHD
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on “ Integrating AI into Aquatic Ecosystem Models to Decode Ecological Complexity ” funded by Villum Fonden. Within that project, the focus is on exploring novel ways to infer information from environmental data