PhD student modelling of human microbiota
Are you looking for a PhD opportunity and are you interested in a challenging research project in human microbiota? Then the role as PhD student ‘modelling of …
- Albinusdreef, Leiden, Zuid-Holland
- Tijdelijk contract / Tijdelijke opdracht
- Uren per week:
- 36 uur
- € 2279 - € 2919 per maand
Are you looking for a PhD opportunity and are you interested in a challenging research project in human microbiota? Then the role as PhD student ‘modelling of human microbiota’ might be for you.
As a PhD candidate in the Department of the Medical Statistics and Bioinformatics you will be part of a project aimed to gain more insight on the functional and compositional properties of the microbial ecosystems of our body (human microbiota). In this multifaceted project you will work at the interface between mathematics, ecology, bioinformatics and medical statistics.
Human health is profoundly impacted by the microbial populations residing on and within the human body (human microbiota). Several diseases are associated with an imbalance (dysbiosis) in these microbiota. The aim of this PhD project is to adapt and extend existing quantitative approaches drawn from systems ecology, to identify the topologies of microbial interaction networks that characterize such imbalance. This knowledge may ultimately be used for directed therapeutic interventions.
Within this project, you will apply a mesh of mechanistic and statistical modelling techniques on a multitude of metagenomics datasets from different anatomical sites of the human body (i.e. gut, respiratory tract). By using different network inference techniques, you will characterize the dynamics and reconstruct the network of microbial interactions. You will apply concepts of complexity theory to investigate the presence of possible alternative stable states and uncover the mechanisms that govern microbiota stability and resilience to disease.
This requires a multidisciplinary approach covering a broad spectrum of expertise. For this purpose, an interdisciplinary consortium of twelve experts from different institutes and universities has been formed specifically for this project and they will work synergically with you during the entire duration of the PhD track.
You are highly motivated and enthusiastic candidate. You hold a master degree in mathematics, theoretical ecology, bioinformatics or a similar discipline. You are interested in microbial communities and have affinity with complex network theory. You possess strong analytical skills, like to work with large datasets and are fluent in at least one programming language (C, C++, Matlab, Python, R). You are well able to communicate with researchers from other disciplines and are a good team player. We also expect you to have excellent written and oral communication skills in English. Recognize yourself in this profile? Then do not hesitate to apply.
You are employed on the basis of a 36-hour week. Appointment will lead to a PhD thesis and is for a maximum duration of four years. Your salary depends on your qualifications and experience, with a maximum of € 2.279 gross per month in the first year, amounting to € 2.919 gross per month in the fourth year based on a full-time position (scale for PhD students, Collective Labor Agreement for University Hospitals).
The terms of employment offered by the LUMC are highly favorable. For example, you will receive 8% holiday remuneration, a year-end bonus, and a pension arrangement with the National Civil Pension Fund. Also, as employee of one of the University Hospitals in the Netherlands, you can benefit from our collective health insurance policy.
Moreover, the LUMC offers excellent facilities in the area of education, child-care center, and career advice.
If you have any question about this position, please contact dr. Elisa Benincà, theoretical ecologist and project manager from RIVM (email@example.com, telephone +31 30 274 8532), or Prof. Jacco Wallinga, Professor in Modelling Infectious Diseases, Department Biomedical Data Sciences, telephone+31 71 526 85 99 (of +31 30 274 25 53)).
References will be requested.