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PhD position Innovative methods to combine heterogeneous data within and across evidence domains of chemical risk assessment

  • Mol, Vlaams Gewest, Belgium

Job description

We are looking for a highly motivated and scientifically excellent PhD candidate with strong (bio)statistical background to work in an international, collegial environment on innovative methods for the assessment of risks of chemicals for human health

People are exposed to chemical substances through a variety of pathways. These chemicals can have harmful effects on human health. Health risks are a combination of the actual exposure to substances through various pathways, their inherent toxicological properties, and the complex interactions between chemical substances and the dynamic and biochemical processes in the human body. Exposure and health impacts in their turn depend on several lifestyle factors (such as diet, stress, physical activity, green space, use of consumer products). Various methods are used to assess the risk of these chemicals, including exposure measurements and models, human biomonitoring, epidemiological studies, toxicological studies (in vivo, invitro, in silico), and risk assessment models. Often these methods only include part of the complex pathways from exposure to health impact. Moreover, studies are often limited in their scope, size,… and will therefore only give partial evidence of the actual risk. To strengthen evidence on harmful effects of specific chemical substances or mixtures of chemicals, there is need to combine and interpret data and results of studies within and across evidence domains.   

The research of your PhD will be embedded in the European Partnership for the Assessment of Risks from Chemicals (PARC). PARC is seeking to develop next-generation chemical risk assessment in order to protect health and the environment. You will be involved in a work package on innovative analytical approaches in chemical risk assessment, aiming at improving the re-use of increasing amounts of open and FAIR data from different studies and disciplines. You will carryout case studies related to the integration of (heterogeneous) FAIR data within and across disciplines by using methods such as pooled (mixture) analysis and(Bayesian) meta-analytical (network) approaches. A first potential case study will focus on methods to study the mediating role of effect biomarkers in the association between pollutant mixtures and health outcomes, using data from human biomonitoring studies. Whereas epidemiological mediation analyses are often limited to a single exposure and mediator, the aim of this case study is to apply innovative methods to integrate information on multiple pollutants, mediators, and health outcomes. A second potential use case on health impact aims at an integrated assessment of metabolomics data measured by different technologies (LC-MS and NMR). Instead of using separate models for the MS and NMR data, this case study will look for methods allowing a combined analysis. You will be involved in the selection of other potential case studies. The main challenge of your PhD will be to select promising methods and implement these on real data in selected case studies. You will publish the findings as papers in peer-reviewed scientific journals. These papers will form the basis of your PhD thesis.

Your PhD will be carried out at VITO in a strategical collaboration between VITO and the University of Hasselt (Data Science Institute). As an internationally recognized research center, VITO wants to accelerate the transition to a sustainable world. With its Health research program, VITO is committed to supporting the societal transitions to a more preventive health system. One of the pillars of this is the prevention of diseases caused by environmental pollution through identification and assessment of health risks and subsequently proposing measures to control them.

How to apply?
Applications should be submitted online and include a copy of your CV, diploma transcripts and a cover letter.
More information about the application procedure is available on the VITO website.
You can register until July 8, 2024 for the jury of  September 25, 2024.

Job requirements

  • A master's degree in (bio-)statistics, bioinformatics, or biological disciplines (e.g. biomedical sciences, medicine, bioengineering science or equivalent) with excellent study results.
  • Experience with different biostatistical methods, including Bayesian models.
  • Programming skills in statistical/mathematical software such as R, SAS, Python,…
  • Basic knowledge of molecular epidemiology.
  • Strong analytical problem-solving skills (creative, critical, and open-minded) and you work accurately.
  • A team player but are also able to work independently.
  • Good oral and written communication skills in English.