The Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB) is a pioneer and a driver of bioeconomy research. We create the scientific foundation to transform agricultural, food, industrial and energy systems into a sustainable bio-based circular economy. We develop and integrate techniques, processes and management strategies, effectively converging technologies to intelligently crosslink highly diverse bioeconomic production systems and to control them in a knowledge-based, adaptive and largely automated manner. We conduct research in dialogue with society, policymakers, industry and other stakeholders - knowledge-driven and application-inspired.

To strengthen the **Department Data Science in Bioeconomy**, we are looking for

**Two Student Research Assistants (m/f/d) (40-80h per month)**

**Focus on Microbiome Data Science and Explainable Machine Learning**

**Core research themes**

We are looking for motivated and skilled students to join our research team in the field of plant microbiome data science. The positions focus on machine learning, explainable AI, and cross-study analysis of large public microbiome datasets. Possible research directions include:

- Prediction of host and ecological metadata from plant microbiome composition, including plant part, microbiome compartment, and host taxonomy
- Cross-study evaluation of microbiome-based prediction models to assess robustness and generalizability across independent datasets
- Explainable machine learning for identifying stable microbial indicators associated with plant tissues, host lineages, and ecological niches
- Comparative analysis of ecological signals encoded in microbiome data, including the relative strength of plant compartment, plant organ, and host effects
- Machine-learning-assisted exploration of microbial interaction and co-occurrence patterns in plant-associated microbiomes.

**Your responsibilities**

- Collaborate with researchers on the design and evaluation of data-driven analyses for plant and soil microbiome datasets
- Support data integration, metadata harmonization, preprocessing, and quality control of large public sequencing datasets
- Implement and benchmark machine-learning models for predicting biological and ecological metadata from microbiome composition
- Apply explainable AI methods to identify robust microbial indicators and interpretable ecological patterns
- Contribute to the analysis of microbial co-occurrence and interaction structures across plant microbiome datasets
- Assist in preparing figures, reports, presentations, and scientific manuscripts
- Participate actively in team meetings and scientific discussions.

**Your qualifications **

- Currently enrolled in Bioinformatics, Data Science, Computer Science, Computational Biology, Statistics, or a related field
- Interest in microbiome research, ecological data analysis, and machine learning
- Good programming skills in Python
- Experience with scientific Python tools such as pandas, scikit-learn, matplotlib, and Jupyter; experience with PyTorch is a plus
- Familiarity with microbiome data analysis, statistics, or bioinformatics workflows is desirable but not required
- Interest in explainable AI, reproducible research, and cross-study data integration
- Very good written and spoken English skills
- Independent, structured working style and strong analytical thinking.

We explicitly encourage applications from students who are still building their expertise in machine learning but are highly motivated to grow in a research-oriented environment. If you are unsure whether you meet all requirements, we still encourage you to apply.

**We offer**

- Cutting edge research on Microbiome and AI
- Opportunities to contribute to scientific publications and open-source frameworks
- Supervision by experienced scientists
- An attractive, research-intensive, and interdisciplinary working environment
- A highly motivated, international team
- Excellent infrastructure for scientific work
- Flexible working hours and excellent equipment
- Family-friendly and flexible working hours and excellent equipment
- A workplace at the edge of a picturesquare landscape, easily aaccessible by bike or public transport.

The position is to be filled with 40 to 80 hours per month and is limited to one year. The salary is depending on your degree (14,59 Euros per hour with Bachelor’s degree, 13,98 Euros per hour without degree).

For further information, please contact** Prof. Dr. Marina Höhne **(E-Mail: [mhoehne@atb-potsdam](https://mailto:mhoehne@atb-potsdam)) and visit our website at [www.atb-potsdam.de](http://www.atb-potsdam.de).

Please apply online **by 19 April 2026,** including a strong motivation letter demonstrating your suitability for the advertised student research assistant position, a detailed CV, all certificates with grades (A levels and the current transcript of Bachelor and/or Master courses and grades), and any recommendation letters via our application portal (**reference number 2026-DS-2**): [https://www.atb-potsdam.de/en/career/vacancies](https://www.atb-potsdam.de/en/career/vacancies). Applications received after the application deadline cannot be considered.

The interviews will take place on 27 and 28 April 2026.

We are committed to equal opportunities. Applications from severely disabled candidates will be given preference if equally qualified.

By submitting your application, you agree that your documents may be stored for up to six months even in the case of an unsuccessful application. Further information on data protection can be found here:
[https://www.atb-potsdam.de/en/data-protection-declaration-for-the-application-process](https://www.atb-potsdam.de/en/data-protection-declaration-for-the-application-process).