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  • Company Syddansk Universitet in Odense M
    10.12.2024 Updated on: 16.12.2024

    A position as a Clinical Professor (50% time) and as a Consultant in (50% time) in medical gastroenterology, inflammatory bowel diseases is vacant as soon as possible at theResearch Unit of Medical Gastroenterology,Department of Clinical Research,University of Southern Denmarkand at theDepartment of Medical Gastrointestinal Diseases, Odense University Hospital, respectively. The posts are considered as one entity.  The position is for 5 years with the possibility of extension. Job description Seethe full job description. Further information  Further information can be obtained from the Head of the Department of Clinical Research, Rikke Leth-Larsen, by e-mail rllarsen@health.sdu.dk or from the Head of the Research Unit Aleksander Krag, by e-mail akrag@health.sdu.dk If a potential applicant ...

  • Company Danmarks Tekniske Universitet in Frederiksberg C
    10.12.2024 Updated on: 16.12.2024

    Algorithmic bias is a fundamental problem when using machine learning and AI to solve problems that involve humans. While monitoring and mitigating algorithmic bias is crucial for the safe and fair utilization of AI, bias measurement becomes an even greater challenge when labels are systematically wrong for certain unfortunate groups. This project aims to document and understand the effect of label bias on machine learning and AI algorithms, and to propose solutions to overcoming label bias both when diagnosing and mitigating AI bias.Responsibilities and qualifications Your main responsibility in the project will be to build create realistic medical imaging test beds for label bias and use them to investigate how label bias affects algorithms, their bias and their bias mitigation. Your pri...

  • Company Danmarks Tekniske Universitet in Kongens Lyngby
    07.12.2024 Updated on: 08.12.2024

    Algorithmic bias is a fundamental problem when using machine learning and AI to solve problems that involve humans. While monitoring and mitigating algorithmic bias is crucial for the safe and fair utilization of AI, bias measurement becomes an even greater challenge when labels are systematically wrong for certain unfortunate groups. This project aims to document and understand the effect of label bias on machine learning and AI algorithms, and to propose solutions to overcoming label bias both when diagnosing and mitigating AI bias.Responsibilities and qualifications Your main responsibility in the project will be to build create realistic medical imaging test beds for label bias and use them to investigate how label bias affects algorithms, their bias and their bias mitigation. Your pri...

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