Job overview

Climate change induced food insecurity and conflict have driven a steady increase in the number of displaced populations globally. These often lack adequate access to national health service including routine childhood vaccination putting an increasing focus on cross border acting NGOs to work with local governments to enhance health services in these hard-to-reach communities.

The successful candidate will use mathematical models and statistical modelling to evaluate and help optimise immunisation approaches to improve health in humanitarian crises. There is substantial scope to develop research interests and study objectives, but topics may include spatial prioritisation of measles campaigns in Niger, impact of reactive vaccination during Diphtheria or Cholera outbreaks, effectiveness and optimal schedules of Malaria vaccination in Chad, Lassa Fever vaccine in West-Africa or Ebola ring vaccination strategies in the Democratic Republic of Congo.

While the position requires a substantial background in programming and infectious disease analytics, there will be the opportunity for additional training. The successful candidate will also be expected to regularly attend scientific conferences and collaborative visits particularly with Médecins Sans Frontières’ (MSF) Epicentre in Paris, France, as well as field visits as opportunities arise.

The successful candidate will come with a high degree of motivation and self-sufficiency and will be expected to work interdisciplinary with multiple international collaborators, particularly Epicentre.

Scientific staff is given sufficient time to carry out their own scientific work in accordance with their employment relationship (§110 (4), 3 BerlHG).

We are looking for

A master’s degree in mathematics, statistics, epidemiology, or an equivalent degree in a subject with a strong analytical and/ or public health component.
Evidence of prior interest in the fields of global health, humanitarian health and epidemiology.
Evidence of substantial experience with programming in R, Python or similar.
Evidence of understanding of the basic concepts of mathematical modelling for infectious diseases as well as of epidemiological measures.
Applicants ideally will also have:

Experience with the implementation and interpretation of mathematical models for infectious diseases.
Experience with fitting those models to data
Experience in the use of version control and collaborative coding
Knowledge of vaccinology in humanitarian emergencies
In your cover letter please elaborate how your skills and interest match each of the listed expectations.