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Field
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university/spinout environment. This is a unique opportunity to work at the forefront of applied research and innovation, helping translate novel control algorithms and hardware prototypes into real-world
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to have the following skills and experience: Essential criteria PhD qualified in relevant subject area Extensive experience working in bioinformatics with large datasets Previous experience in statistical
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to have the following skills and experience: Essential criteria PhD qualified in relevant subject area Extensive experience working in bioinformatics with large datasets Previous experience in statistical
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this role, we are looking for candidates to have the following skills and experience: Essential criteria PhD qualified in relevant subject area Extensive experience working in bioinformatics with large
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develops new intellectual understanding Disseminate research findings for publication, research seminars etc Supervise students on research related work and provide guidance to PhD students where appropriate
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. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in English Desired qualifications: Experience with research on epidemiological modelling, with an emphasis
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to research-based activities, including the development of new data analysis algorithms, processing and analysis of field data, and participation in the fieldwork. Your responsibilities will include: Conduct
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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algorithms, including machine unlearning techniques, to enhance model robustness and reliability. Design and execute rigorous AI testing frameworks to assess and mitigate risks in AI systems. Collaborate with
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. The successful applicant will use state of the art inference algorithms to design, use and share the findings of epidemiological models that integrate across large and diverse datasets including capture-mark