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Diseases Agency Singapore (CDA), the National Environment Agency Singapore (NEA), the Machine Learning & Global Health Network (MLGH), and wider regional and global partners. This is a senior scientific role
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in mathematical modelling and Bayesian inference while learning from three collaborating chief investigators. You will also build your publication record and professional networks through seminars
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to have a strong background in the foundations of machine learning. Special Instructions Required application documents include a cover letter, CV, a statement of research interests, and up to three
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funds can be used effectively and on time. Research Project: This fellowship will provide an opportunity to learn alongside the Crisis Cooperative Agreement and Innovation (CCI) Team. The CCI Team manages
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have excellent computer skills including Microsoft Office proficiency and the ability and desire to learn software and reporting systems. Preferred Qualifications: Formal Education · Bachelor’s degree
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at least a PhD Degree in Epidemiology, Public Health, Environmental Health, Biological Sciences, Biostatistics, Data Science, preferably with relevant experience. Prior experience with machine learning is a
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. Learning Objectives: During the fellowship, under the guidance and mentorship of the mentor and senior research staff, you will: Learn how different environmental stress and biotic factors affect tree
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communities, testing dietary and other control modalities, as part of the learning experience. This will include various culture methods, nucleic acid extraction, PCR methods, sequence analysis
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& Professional Learning team. • Access to on-site childcare, shopping discounts and travel schemes are also available. And much more! To explore the post further or for any queries you may have, please contact
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, and AI/machine learning would be helpful for the role. Experience with participant recruitment and retention as well as clinical human subject studies is a plus. Special Instructions Application