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experimental independence, including the design and execution of animal studies, development and optimization of molecular and cellular assays, and data analysis and interpretation. Candidates should be
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) outlining a research project addressing the history of Danish botany and the Flora Danica volumes in the period 1840–1900 within the statement of future research plans and information about research
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supervision of research projects in the field of veterinary virology • Acquisition of third-party funding • Dissemination, presentation and publication in peer-reviewed journals of scientific data • Independent
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Requisition Details GENERAL REQUISITION INFORMATION EEO Statement: UNMC is an Equal Employment Opportunity Employer, including an equal opportunity employer of protected veterans and individuals
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Robot Learning (ID: TUEILSY-POSTDOC20251219-RL) Robots that learn from data promise greater autonomy and performance, but their deployment in the real world hinges on the ability to guarantee safety
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Postdoctoral Researcher Position in Ecological Knowledge-Guided Machine Learning at Aarhus Univer...
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
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across connected forest–lake ecosystems. By integrating multi-taxa field data, trait-based ecology, experiments, and advanced statistical analyses, TRACE aims to uncover how ecological processes propagate
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measure the impact of wood-aging on a range of foodstuffs. This portion of the project involves undertaking final validation experiments and refining the data analysis pipeline, before creating a final
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molecular biology analyses. May assist and supervise graduate and undergraduate students. 25% Cloning molecular biology 25% Plant bioassay 20% Record and analyze data/results 10% report research through
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep