Research Associate in Wildlife Sciences (Georg August University of Gottingen)

The Department of Wildlife Sciences at the Faculty of Forestry and Forest Ecology at the Georg August University of Göttingen is seeking to fill a position as a research associate (f/m/d) in a third-party funded project on landscape genomics of red deer. This is a 100 % position with regular weekly working hours (currently 39.8 hours) for an initial period of three years. Remuneration will be based on salary group 13 TV-L (German public service agreement). The position should start no later than February 1, 2026. Job Description The successful candidate will conduct a landscape genomic study for red deer (Cervus elaphus) in Lower Saxony and neighboring areas. The occurrence of red deer in Germany is limited to a few spatially isolated subpopulations. Population fragmentation has already led to a loss of genetic diversity. However, to date, all genetic studies on red deer in Germany have been carried out using microsatellites. For the project, approximately 2,000 samples will be collected and analyzed by partners at the University of Giessen using microsatellites. These same samples will then be used for SNP-based analysis in our department. Sequencing will be performed by an external service provider using either an existing SNP array for red deer, or via RADseq, ddRAD, or Genotyping By Sequencing. The bioinformatic steps will run either on the University of Göttingen's High Performance Computing Cluster (see https://gwdg.de/en/hpc/) or via a cloud service provider. The focus of the position is on bioinformatics and the population and landscape genetic analysis of SNP data. The goal is to quantify the neutral and adaptive genomic diversity of red deer populations, their genetic exchange, genetically effective population sizes, and inbreeding, as well as to identify possible landscape influences on these parameters. The SNP-based results will then be compared with those from the microsatellites. In addition to the samples from Lower Saxony, an interesting dataset of over 900 red deer samples from across Germany is available, which have already been sequenced in an external laboratory using an Illumina NovaSeq 6000 S4 PE150. This dataset can also be analyzed for landscape genetics as part of the project. For comparison purposes, a dataset with 14 microsatellites is already available for the same samples. There is also the opportunity to participate in a project in which a SNP chip is used to calculate relationships within a red deer population using a genomic relatedness matrix (GRM). Your Profile • Completed master's degree in bioinformatics, genetics, evolution, ecology, biology, or a similar field • Experience in the preparation and analysis of SNP datasets and the required bioinformatics methods • Experience in working with high-performance/remote computing clusters • Good knowledge of population genetic concepts and methods • Good knowledge of landscape genetic or genomic concepts and methods • Knowledge of spatial data analysis (e.g., modeling resistance landscapes for gene flow) in R and/or a GIS • Good programming skills in R, Perl, and/or Python • Very good written and spoken English skills • Experience in scientific writing, demonstrated by (co-)authorship of peer-reviewed articles in scientific journals Further advantages include a doctorate in a field relevant to the project, knowledge of calculating genomic kinship matrices, experience in collaborative projects, and knowledge of other programming languages (e.g., Python). Java, MatLab, Julia, LaTex, Mathematica, C, SAS). We are seeking an enthusiastic and productive individual who is independent but also a good team player. You should be motivated to advance and shape future landscape genomics research in the department. The work location is Göttingen. The University of Göttingen strives to increase the proportion of women in fields where women are underrepresented and therefore strongly encourages qualified women to apply. It is a family-friendly university and promotes the compatibility of science/work and family. The university is particularly committed to supporting employees with severe disabilities and therefore welcomes applications from people with severe disabilities. In cases of equal qualifications, applications from people with severe disabilities will be given preference. To safeguard interests, any disability or equal opportunity must be included in the application. Please send your comprehensive, English-language application (a letter of motivation with the usual supporting documents) electronically as a single PDF document to Niko Balkenhol (nbalken@gwdg.de) by June 29, 2025. In your letter of motivation, you should primarily describe your experience in working with SNP datasets, bioinformatics, and landscape genetic and genomic analyses. For further inquiries, please contact Prof. Dr. Niko Balkenhol (nbalken@gwdg.de). Please note: With submission of your application, you accept the processing of your applicant data in terms of data-protection law. Further information on the legal basis and data usage is provided in the https://uni-goettingen.de/GDPR If you apply for this position please say you saw it on Bioloxy

The Department of Wildlife Sciences at the Faculty of Forestry and Forest Ecology at the Georg August University of Göttingen is seeking to fill a position as a

research associate (f/m/d)

in a third-party funded project on landscape genomics of red deer.

This is a 100 % position with regular weekly working hours (currently 39.8 hours) for an initial period of three years. Remuneration will be based on salary group 13 TV-L (German public service agreement). The position should start no later than February 1, 2026.

Job Description

The successful candidate will conduct a landscape genomic study for red deer (Cervus elaphus) in Lower Saxony and neighboring areas. The occurrence of red deer in Germany is limited to a few spatially isolated subpopulations. Population fragmentation has already led to a loss of genetic diversity. However, to date, all genetic studies on red deer in Germany have been carried out using microsatellites.

For the project, approximately 2,000 samples will be collected and analyzed by partners at the University of Giessen using microsatellites. These same samples will then be used for SNP-based analysis in our department. Sequencing will be performed by an external service provider using either an existing SNP array for red deer, or via RADseq, ddRAD, or Genotyping By Sequencing. The bioinformatic steps will run either on the University of Göttingen's High Performance Computing Cluster (see https://gwdg.de/en/hpc/) or via a cloud service provider. The focus of the position is on bioinformatics and the population and landscape genetic analysis of SNP data. The goal is to quantify the neutral and adaptive genomic diversity of red deer populations, their genetic exchange, genetically effective population sizes, and inbreeding, as well as to identify possible landscape influences on these parameters. The SNP-based results will then be compared with those from the microsatellites.

In addition to the samples from Lower Saxony, an interesting dataset of over 900 red deer samples from across Germany is available, which have already been sequenced in an external laboratory using an Illumina NovaSeq 6000 S4 PE150. This dataset can also be analyzed for landscape genetics as part of the project. For comparison purposes, a dataset with 14 microsatellites is already available for the same samples.

There is also the opportunity to participate in a project in which a SNP chip is used to calculate relationships within a red deer population using a genomic relatedness matrix (GRM).

Your Profile

• Completed master's degree in bioinformatics, genetics, evolution, ecology, biology, or a similar field
• Experience in the preparation and analysis of SNP datasets and the required bioinformatics methods
• Experience in working with high-performance/remote computing clusters
• Good knowledge of population genetic concepts and methods
• Good knowledge of landscape genetic or genomic concepts and methods
• Knowledge of spatial data analysis (e.g., modeling resistance landscapes for gene flow) in R and/or a GIS
• Good programming skills in R, Perl, and/or Python
• Very good written and spoken English skills
• Experience in scientific writing, demonstrated by (co-)authorship of peer-reviewed articles in scientific journals
Further advantages include a doctorate in a field relevant to the project, knowledge of calculating genomic kinship matrices, experience in collaborative projects, and knowledge of other programming languages (e.g., Python). Java, MatLab, Julia, LaTex, Mathematica, C, SAS).
We are seeking an enthusiastic and productive individual who is independent but also a good team player. You should be motivated to advance and shape future landscape genomics research in the department.

The work location is Göttingen.

The University of Göttingen strives to increase the proportion of women in fields where women are underrepresented and therefore strongly encourages qualified women to apply. It is a family-friendly university and promotes the compatibility of science/work and family. The university is particularly committed to supporting employees with severe disabilities and therefore welcomes applications from people with severe disabilities. In cases of equal qualifications, applications from people with severe disabilities will be given preference. To safeguard interests, any disability or equal opportunity must be included in the application.

Please send your comprehensive, English-language application (a letter of motivation with the usual supporting documents) electronically as a single PDF document to Niko Balkenhol (nbalken@gwdg.de) by June 29, 2025. In your letter of motivation, you should primarily describe your experience in working with SNP datasets, bioinformatics, and landscape genetic and genomic analyses.

For further inquiries, please contact Prof. Dr. Niko Balkenhol (nbalken@gwdg.de).

Please note:
With submission of your application, you accept the processing of your applicant data in terms of data-protection law. Further information on the legal basis and data usage is provided in the https://uni-goettingen.de/GDPR