Wastewater is increasingly recognized as an information-dense footprint of the urban microbiome. But how can this be leveraged to improve public health?
Our ambition is to explore the microbial and chemical composition in wastewater, including commensal and pathogenic microbes, to identify interrelations to human health and inform strategies to improve human well-being. To this end, we will establish methods to characterize a broad range of microbes and chemical compounds from wastewater, as a proxy for the exposome of the population in the respective catchment. This spatiotemporal data will be intersected with available health, demographic and socioeconomic data to explore and identify patterns, associations and interactions between environmental and human health indicators. The expected results will support fundamental research questions about infectious diseases and drive concrete initiatives to positively impact public health.

We are looking for a highly motivated postdoc candidate (f/m) who seeks to apply her/his strong background in data science to pertinent questions of infectious disease and pathogen surveillance. S/he will carry out research and coordinate the collaborative efforts of this ambitious multidisciplinary project. The candidate should be experienced in statistical and machine-learning methods to dissect large, multidimensional, time and space resolved data. Interests in microbiology and public health are beneficial.

Excellent communication skills also to colleagues from other fields (e.g., public health, biology, demography) will be a required. High intrinsic motivation and the ability to think outside-of-the-box are desired. The successful candidate will become part of the international team of wet and dry lab biologists within the Bergthaler Lab (http://www.bergthalerlab.org). The Bergthaler Lab and the Cluster of Excellence provide a highly supportive and diverse environment at the Medical University of Vienna in Vienna, a leading academic institution, located in one of the most liveable cities in the world.

Selected references: Amman F et al. Nature Biotechnology 2022 (doi: 10.1038/s41587-022-01387-y), Popa A et al. Science Translational Medicine 2020 (doi: 10.1126/scitranslmed.abe2555), Keshaviah A et al. Lancet Global Health 2023 (doi: 10.1016/S2214-109X(23)00170-5.).

• Proficiency  in working in a Linux-based HPC environment
• Data analysis and appropriate tools (e.g., R, python)
• Multivariate statistics and Machine-learning
• (Bioinformatic) analysis of metagenomic and environmental data
• Excellent communication and project management skills 
• Intrinsic drive for excellence and towards groundbreaking work at the intersection of microbiology and public health