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-disciplinary environment.A solid track record of scientific publications and conference presentations.Capacity to work independently and in collaboration with team members.Preferred Qualifications:Familiarity
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University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived experiences. Embracing varying opinions and perspectives
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institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development of Morocco and Africa
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About the Role We are seeking an enthusiastic and motivated postdoctoral researcher to apply advanced data analytics and machine learning techniques to real-world clinical data in the field of viral
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on understanding the spread and control of human infectious diseases using modelling and pathogen genomics. This is a short-term opportunity to apply machine learning methods to two key projects. First, you will
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Work Package 4 that aims to integrate heterogeneous data acquired through Work-packages 1 to 3 and decipher genome–phenome relationships, working in close collaboration with a PhD student working on
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scientific discovery. You will join a supportive, collaborative and productive research environment that encourages independence and fosters career development. Some key skills required: A PhD in immunology or
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Conduct independent and collaborative research within one or more of the D4H thematic areas Contribute to the development of training materials and workshops for doctoral candidates and facilitate exchange
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multimodal deep learning models combining CT imaging and clinical data, trained on the unique RADAR consortium database . These models will be validated in close collaboration with clinical and industrial
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 4 hours ago
publishing of results. (20%) Collaborate with other team members on various lab projects (20%) Minimum Acceptable Education & Experience: PhD in quantitative ecology, marine science, or related field Required