Master Thesis »Biogas optimization through CSMCA and machine learning«

As one of 76 institutes of the Fraunhofer-Gesellschaft we show how to not only master crises, but grow from them. Pushing boundaries. Always trying something new. Not just thinking, but thinking ahead. We turn research into the future.

We want an earth that is worth living on. With our research in the divisions of climate-neutral energy systems, resource-efficient processes and circular products, we make concrete contributions to achieving the 17 Sustainable Development Goals (SDGs) of the United Nations. Our employees conduct research in the fields of energy, environment, safety, health, communication and mobility.

The focus of this master's thesis is to develop a novel method that integrates Context-Specific Metabolic Coherence Analysis (CSMCA) with machine learning-based approaches to utilize Genome-Scale Metabolic Models (GEMs) and multi-omics data for optimizing biomethane production in biogas plants. Specifically, the study aims to investigate anaerobic digestion processes in biogas plants using large-scale datasets, including metagenomic, metatranscriptomic, and environmental data collected from full-scale biogas digesters.

What you will do

• Develop and amend metabolic models for relevant microorganisms found in biogas systems using metagenomic data obtained from biogas reactors

• Apply flux balance analysis (FBA) and flux variability analysis (FVA) to simulate a previously constructed genome-scale metabolic model (GEM) and assess the impact of changing environmental factors (e.g., ammonia, volatile fatty acid (VFA) concentrations) on microbial fluxes and biogas production.

• Integrate multi-omics data (mainly transcriptomes) to optimize the previously simulated GEM and model the context-specific activity of microbial metabolism under various operating conditions (e.g., pH, temperature, organic loading rates). Apply CSMCA to refine GEM predictions and enhance the accuracy of biogas process optimization, particularly in methanogenesis and other key metabolic pathways

• Employ machine learning algorithms to dynamically optimize processes and predict ideal environmental conditions and microbiome compositions to maximize biomethane production

• Validate the developed and optimized models by comparing predictions with measured data from an operational full-scale biogas digestor.

Expected outcome:

a) Context-specific GEMs for microbial taxa involved in biogas production.

b) Enhanced operational strategies through deeper understanding of microbial interactions and metabolic diversity within biogas reactors.

c) Predictive models for optimizing methane yields.

What you bring to the table

• Current master's degree in biology, biochemistry & bioinformatics 

• Experience in the fields of bioinformatics, computational biology or systems biology

• Proficiency in a programming language (e.g. Python or R) for data analysis and model implementation

• Ability to work with large biological data sets and a basic understanding of the processes involved in biogas production

• Strong communication skills, preferably English at C1 level with basic knowledge of German

• Initial experience with metabolic modeling tools and frameworks (e.g. COBRA Toolbox) is desirable

What you can expect

• As part of a dynamic team, you actively drive sustainable solutions for a resource-efficient future
• Opportunity to combine your knowledge from your studies with practical experience at the interface between business and science and to integrate your own ideas
• New Work, diversity and cooperative interaction are important components of our strategic orientation
• Flexible working hours - we know that your studies come first 
• Flexible work locations that fit your studies - mobile work and on-site work are combined as needed
• Intense support for the preparation of your final thesis
• Health and mindfulness programs for good balance
• You can find even more attractive benefits here: Fraunhofer UMSICHT as employer

We value and promote the diversity of our employees' skills and therefore welcome all applications - regardless of age, gender, nationality, ethnic and social origin, religion, ideology, disability, sexual orientation and identity. Severely disabled persons are given preference in the event of equal suitability. Remuneration according to the general works agreement for employing assistant staff.

With its focus on developing key technologies that are vital for the future and enabling the commercial utilization of this work by business and industry, Fraunhofer plays a central role in the innovation process. As a pioneer and catalyst for groundbreaking developments and scientific excellence, Fraunhofer helps shape society now and in the future. 

Interested? Apply online now. We look forward to getting to know you! 

We would like to point out that applications are only accepted via our career portal. For data protection reasons, we cannot accept applications by e-mail. Please submit at least the following documents:

• Curriculum vitae
• Current certificate of enrollment
• Current transcript of records
• Bachelor's certificate

Please do not hesitate to contact us if you have any questions regarding this position:

Nestor Patient Tchamba Sefekme +49 208 8598-1410

Dr. Frederik Koepsell +49 208 8598-1786
bewerbung@umsicht.fraunhofer.de

Fraunhofer Institute for Environmental, Safety, and Energy Technology UMSICHT 

www.umsicht.fraunhofer.de 

Requisition Number: 78794