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on conduction system diseases. To identify these pathways, the postdoc will analyze and integrate data from mouse models for different diseases, human tissue, human iPSC-derived pacemaker-like cells and cutting
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, including quantitative, qualitative, and experimental approaches. Ideally, experience with Python, Stata, or R. • demonstrates proficiency in data collection, processing, and analysis, preferably using
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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
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to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services
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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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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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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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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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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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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