Post Doctoral Scientist - Infectious Disease Modelling

Key responsibilities

The Post Doc will have sufficient knowledge on vector-borne diseases, infectious disease modelling, and applied computing to enable him/her to lead the development of robust prediction models that balances the need for simplicity and accuracy. Despite this expectation, the post doc will have opportunities to develop his/her skills further through training, mentorship or any other relevant competency development activities. The Post Doc will work in a multidisciplinary team of local and international researchers, and s/he will:   

• set up research questions and analytical frameworks that will guide the development of dynamic models for studying transmission patterns of the vector-borne zoonotic diseases that have been prioritised for this work. The analytical frameworks will be developed in consultation with local and international partners and stakeholders
• develop process-based spatially explicit vector-host-pathogen mathematical or simulation models and link these with decision support frameworks where policy-relevant outputs such as dynamic risk maps are displayed
• apply statistical and computational methods to available primary and secondary data to generate input parameters for the dynamic models that will have been developed
• participate in webinars, conferences, journal clubs and stakeholder meetings where s/he will be expected to share research findings, lessons learnt, or new research leads based on the work s/he will be leading
• develop new research proposals in partnership with the project team members to extend the work s/he will be leading be given adequate opportunities to enrol for professional trainings to build their competencies on modelling and data science

Requirements

• A doctoral degree in epidemiology, applied statistics or public health in the previous 5 years from an accredited college or university
• PhD candidates who are at the final stages of their program (e.g. awaiting graduation) will also be considered 
• Strong quantitative skills including statistical, systems dynamic, and simulation modelling
• Strong programming skills using the commonly used languages e.g. R, Python.
• A track record of publications. Higher weights will be allocated to publications where the candidate is the first author
• Excellent knowledge of the English language both in speaking and in writing.
• Experience on training and mentoring students, junior scientists or project partners
• Supervising the design and implementation of research projects