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Place of work: Leipzig
The Helmholtz Centre for Environmental Research (UFZ) with its 1,100 employees has gained an excellent reputation as an international competence centre for environmental sciences. We are part of the largest scientific organisation in Germany, the Helmholtz association. Our mission: Our research seeks to find a balance between social development and the long-term protection of our natural resources.
Heterogeneity is revealed as differentiation of class representatives (plants, animals, microbes) in their features (size, wellness, activity). Reliable methods of heterogeneity quantitation are required to optimize biotechnological processes, to study factors effecting the characteristics of living organisms, their adaptation to environmental changes and evolution.
Parameters of normal distribution like Coefficient of Variation (CV) may be successfully employed as heterogeneity measure when species show a unimodal distribution in their characteristics within a single population. To quantify the heterogeneity of multimodal distributions over several subpopulations, the Cumulative Differentiation Tendency Index (CDTI) is derived from a characteristic rank distribution approximated with the Zipfian power function. The Zipfian exponent is considered as Differentiation Tendency Index (DTI) for each subpopulation. The robustness of DTI and CDTI as heterogeneity indices has recently been proved on the single-cell-resolved data sets of SIP-nanoSIMS, optical microscopy and flow cytometry experiments https://doi.org/10.3389/fmicb.2019.02814.
Unsupervised recognition of subpopulations and automated Zipfian approximation of multimodal rank distribution are the remaining challenges to be tackled in order to make the suggested heterogeneity quantitation technique available for a broad application.
The Department of Isotope Biogeochemistry offers the following position:
Master thesis: Implementation of Heterogeneity-quantitation methods (m/f/x)
Subject: Implementation of Heterogeneity-quantitation methodsStarting date: as soon as possible
- Implementation of unsupervised subpopulation recognition (i.e., data clustering)
- Optimization of cluster number (e.g., robust K-mean)
- Implementation of automated data approximation with the Zipfian power function
- Bachelor in IT- Field
- Experience in programming with Python or R
- Knowledge/interest in numerical data evaluation
- Enthusiasm and initiative at work
- Good will to work in an interdisciplinary team
- English (verbal and written)
- A close and good care
- Exciting insights into the work of a leading institute
- Excellent analytical and computing facility
- Work in inter-disciplinary, multinational teams
- The possibility to introduce your own ideas and impulses from the beginning