Cluster of Excellence Work Package: WP 3.2

Duration of contract: 4 years 
Planned starting date: ASAP 
Place of work: University of Vienna
Main supervisor: Sarah Pati
Supervision team: Thilo Hofmann, Christine Moissl-Eichinger, Andreas Richter

Project description:

This subproject aims to investigate the transformation of emerging pollutants, such as tire additives, pharmaceuticals, and consumer products, by oxygenase enzymes in various microbiomes. Oxygenase enzymes play a crucial role in degrading pollutants in the environment by transforming a wide range of compounds into more polar and bioavailable products. However, under certain conditions, these enzymes exhibit poor efficiency in oxygen utilization, leading to the unintended production of reactive oxygen species (ROS). The PhD candidate in this project will perform exposure experiments with emerging pollutants and microbial communities from soil, freshwater, wastewater, and the human lung. In addition, methods will be developed to identify transformation products and quantify ROS. The outcomes of these experiments will determine whether a negative impact of (emerging) pollutants on environmental and human microbiomes can arise not only from the toxicity of the pollutants and their transformation products but also from the production of ROS by oxygenase enzymes. The ideal candidate has a background in environmental or analytical chemistry and a keen interest in studying organic pollutant transformation with high-resolution mass spectrometry and/or stable isotope techniques.

Cluster of Excellence Work Package: WP 7.3

Duration of contract: up to 4 years 
Planned starting date: Summer 2024 
Place of work: University of Vienna
Main supervisor: Dagmar Wöbken
Supervision team: Christina Kaiser, Holger Daims, David Berry

Project description:

Microorganisms in drylands have to endure long periods of drought, interrupted by unpredictable and very short periods of rain. Dormancy – an inactive state or a state of reduced metabolic activity – has long been regarded as a prerequisite for desert soil microorganisms to survive such drought periods. However, as dormancy cannot be sustained indefinitely, phases of resuscitation must also play an important role for long-term survival of desert soil microorganisms and thus for maintaining microbial diversity in one of the harshest environments on the planet.

In this project, we are investigating the mechanisms of desert soil microorganisms that allow desiccation survival and resuscitation. This will be achieved by applying genome-resolved metatranscriptomics of desert soil microbial communities. In situ community transcription patterns will be combined with single-cell activity assays (such as heavy water-NanoSIMS) to detect anabolically active cells and process measurements.

The ideal candidate for this position should have a background in microbiology or microbial ecology and experience in molecular tools to investigate diverse microbial communities (such as amplicon sequencing). The candidate should be excited to apply cutting-edge molecular approaches to identify active community members (i.e. via stable isotope probing) and interested in -omics data analyses. 

Cluster of Excellence Work Package: WP 6.2

Duration of contract: 4 years 
Planned starting date: ASAP
Place of work: University of Vienna
Main supervisor: Petra Pjevac
Supervision team: Andreas Richter, Stephan Krämer, Peter Hinterdorfer, Thomas Böttcher

Project description:

The PhD Student will investigate the effect of soil microorganisms and mineral/rock preparations (type of rock powder, grain size, aggregation state) on enhanced silicate weathering rates and weathering product. Mineral bag experiments will be performed in situ on the field to investigation microbe-mineral interactions on the micro-scale, including weathering, secondary mineral formation, and depleted layer formation in relationship to microbial colonization, and to beneficial identify microbial consortia that enhance weathering. Experimental setups will include microbial cultures and agricultural soil microcosm, where effects of microbial colonization, inoculations and microbiome transfer on enhanced silicate weathering and carbon sequestration will be analyzed and quantified.

Cluster of Excellence Work Package: WP 7 - PhD (I)

Duration of contract: 4 years 
Planned starting date: Fall 2024 
Place of work: University of Vienna
Main supervisor: Shaul Pollak

Project description:

The goal of this project is to develop efficient computational approaches that rely on modern machine-learning concepts to extract biological, ecological, and evolutionary knowledge from the large amount of sequencing data produced by the different work packages in the “MicroPlanet” Cluster of Excellence. Since the early 2000s petabytes of sequencing data have been produced, but the biological insights promised at the onset of the sequencing revolution have not been delivered yet. The sheer diversity of microbial life, with estimated billions of species and trillions of gene families limits our quest for insights, as most genes and species are uncharacterized, and it would take many decades of laborious experimentation to close this gap. Recent advances in Machine learning such as the advent of Large Language Models and tools like AlphaFold have exposed the utility of large-scale data analysis in breaking traditional barriers in biological research. The cluster of excellence offers a unique opportunity to synthesize large volumes of data from across ecosystems to answer fundamental biological questions, but efficient computational tools that go beyond the state-of-the-art are still needed for this task. This project will be in close collaboration with the experimental labs in the Cluster of Excellence and will develop new approaches and tools to analyze time-series data, bacterial genomic data, protein variation and evolution, and making theory-guided inferences about ecology from the underlying genetic and taxonomic structure of microbial communities. Candidates from Computer Science / Physics / Mathematics background with experience in machine-learning are encouraged to apply.

Cluster of Excellence Work Package: WP 7 - PhD (II)

Duration of contract: 4 years 
Planned starting date: Fall 2024 
Place of work: University of Vienna
Main supervisor: Shaul Pollak

Project description:

The ultimate goal of this project is to develop a theory that unites ecosystems through the lens of microbial metabolism and provides a quantitative and testable framework to analyze patterns in species and metabolite abundances across the different work packages of the Cluster of Excellence. The internal metabolism of each microbe is made up of thousands of co-dependent reactions that are far from equilibrium and change with time and depend on the environment, which can also change in small time and length-scales. A detailed description of all metabolic transformations in a microbial community composed of billions of individuals from thousands of species is unfeasible, and a new paradigm is needed. Recent works that used simplified representations of bacterial physiology identified fundamental tradeoffs that arise from cellular resource investment into energy production, biomass generation, and maintenance at the individual level. This provides a fertile ground to incorporate coarse-grained physiology into an ecological framework considering the co-habitation of other metabolic strategies, microbe-microbe interactions, different and varying environmental conditions such as stoichiometry of nutrients and energy availability, and so on. This will build on the rich community and ecosystem ecological theory literature, the metabolic theory of ecology, network and complexity science, as well as statistical physics and thermodynamics. Candidates with strong theoretical backgrounds and experience in modeling complex biological systems using tools from statistical physics are encouraged to apply.