Postdoctoral Scientist in infectious disease modelling and machine learning (80-100%)

The Swiss Tropical and Public Health Institute is the biggest institution for public health, international health and tropical medicine in Switzerland. The institute with over 700 employees delivers a sustainable contribution with its research, education and services to the improvement of health worldwide.

The Department of Epidemiology and Public Health (EPH), within the Swiss Tropical and Public Health Institute, develops and applies epidemiological, statistical and mathematical methods to advance innovation, validation, and application in the field of public health. Within the Infectious Disease Modelling Unit of EPH we are looking for a:

Postdoctoral Scientist in infectious disease modelling and machine learning (80-100%)
We are seeking a postdoctoral scientist for an exciting opportunity to develop disease models and apply machine learning algorithms to support decision making in health. You will undertake modeling and simulation to predict the impact of novel interventions on transmission and disease burden to provide evidence for product development decisions along the clinical development pathway through to implementation. The post will involve using and developing disease models, as well as machine learning algorithms as new approaches to analyze large number of simulations.
You will be joining a multidisciplinary team providing evidence to decision makers to support optimizing and predicting the likely impact of new interventions against malaria, supervised by Professor Melissa Penny. There are many issues concerning the assessment of new interventions, control and elimination of malaria that can only be answered through quantitative analysis, disease modelling and simulation.
We are looking for candidates with:

  1. Strong mathematical and statistical modeling skills; with preference for demonstrated use of applied machine learning algorithms
  2. Strong programming skills (in at least one of R, C++, or Python);
  3. Essential: PhD in Mathematics, statistics or related discipline. e.g.: quantitative epidemiology, ecology modelling;
  4. Expertise/background and interest in areas of infectious disease modelling, epidemiology, public health analysis;
  5. Understanding of the epidemiology of parasitic diseases, especially malaria.
  6. Ability to deliver high quality research and to publish in peer reviewed journals
  7. Ability to communicate effectively in spoken and written English, with good presentation skills;
  8. Ability to work independently and as part of an interdisciplinary team on large research projects in a culturally diverse environment;
  9. Ability to initiate, plan, implement and deliver programs of work to tight deadlines
    Applicants with previous expertise in infectious disease modelling are especially encouraged to apply.
    The post will be based at the Swiss TPH in Basel and the successful applicant will receive a two year contract with possibility of extension. Salary will commiserate with experience (as a minimum based on the Swiss National Science Foundation Postdoc salary scale). This is intended to be a full-time (100%) position, but candidates hoping to work part-time may be considered.

The closing date for applications is 29th of September 2019, but applications will be considered as soon as submitted.

We look forward to receiving your online application with CV, motivation letter, and the names and addresses of 2-3 referees. Note that we can only accept applications via our online recruiting tool https://recruitingapp-2698.umantis.com/Jobs/All. Applications via email will not be considered.

For further information about the position please visit our website https://www.swisstph.ch/en/about/eph/infectious-disease-modelling/ or contact
Professor Melissa Penny melissa.penny@unibas.ch

Type
Postdoc
Institution
Swiss Tropical & Public Health Institute
City
Basel/Lausanne
Country
Switzerland
Closing date
September 29th, 2019
Posted on
August 29th, 2019 15:01
Last updated
August 29th, 2019 15:01
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