PhD Candidate in Molecular and Computational Plant Biology

20.12.2024, Wissenschaftliches Personal

The ERC project “RESIST” at the Professorship for Computational Plant Biology at the TUM School of Life Sciences is seeking a PhD candidate for an interdisciplinary project investigating phenomics and transcriptomics related to drought stress in oats.

The ERC project “RESIST” (https://www.mls.ls.tum.de/en/cpb/research/projects/) at the Professorship for Computational Plant Biology at the TUM School of Life Sciences is seeking a PhD candidate for an interdisciplinary project investigating phenomics and transcriptomics related to drought stress in oats. This exciting role combines molecular biology approaches, computational analyses, and cutting-edge phenotyping technologies in collaboration with Helmholtz Munich (Environmental Simulation Unit, Prof. Jörg Peter Schnitzler).

The ideal candidate will have:

• A Master’s degree in molecular and/or computational plant sciences or a related field. • Experience with molecular biology techniques (wet lab skills).
• Strong interest in interdisciplinary and collaborative research.
• Willingness to work at multiple sites (Weihenstephan and Neuherberg).
• Proficiency in programming languages (e.g., Python, R, bash) and experience with high-performance computing (HPC).
• Expertise or interest in processing large datasets and applying statistical methods.
• Fluency in English, both written and spoken.

As part of a collaborative effort between the Computational Plant Biology group at TUM, a dynamic team focused on plant genomics and agricultural crops, and the Environmental Simulation Unit at Helmholtz Munich led by Prof. Jörg Peter Schnitzler, you will contribute to research on drought stress resistance in oats. Key responsibilities include:

• Investigating drought stress tolerance in oats using phenomics and transcriptomics approaches.
• Conducting greenhouse experiments utilizing cutting-edge phenotyping platforms (e.g., HTP shoot/root platform with 3D multispectral scanning).
• Performing field and greenhouse phenotyping, including measuring traits such as shoot length, root mass, and water-use efficiency (WUE).
• Processing and analyzing RNA sequencing (RNA-Seq) data, from RNA extraction to computational analyses (e.g., gene expression profiling, co-expression networks, and pathway enrichment).
• Employing genetic mapping (e.g., GWAS, SV-GWAS) to identify loci associated with drought stress tolerance.
• Supervising and teaching students in a collaborative academic environment.

• Salary: E13 TV-L (65%).
• The position is initially limited to three years.
• Access to state-of-the-art facilities and resources at the TUM School of Life Sciences and Helmholtz Munich.
• The opportunity to participate in cutting-edge research in plant genomics and drought stress tolerance.
• Integration into a vibrant and international research community at TUM and Helmholtz Munich.
• The position is suitable for individuals with disabilities. Severely disabled candidates will be given preference if their qualifications are otherwise equivalent.
• TUM privacy policy on collecting and processing personal data: https://portal.mytum.de/kompass/datenschutz/Bewerbung/. By submitting your application, you confirm that you have read and understood the data protection guidelines of TUM.
• TUM aims to increase the proportion of women in its workforce and explicitly encourages women to apply.

• Please send your application, including at least one reference, a CV, and all other relevant documents by email to Prof. Nadia Kamal: n.kamal@tum.de.
• Combine all documents into a single PDF file named lastname_phd_resist.pdf. Applications that do not adhere to this format will not be considered.
• Application deadline: January 31, 2025

• Technical University of Munich
• Computational Plant Biology
• Prof. Dr. rer. nat. Nadia Kamal
• Am Staudengarten 2, 85354 Freising
• Tel.: +49 8161 71 5301
• sekretariat.cpb@ls.tum.deå
• www.mls.ls.tum.de/en/cpb
• www.tum.de

The position is suitable for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance.

Data Protection Information:
When you apply for a position with the Technical University of Munich (TUM), you are submitting personal information. With regard to personal information, please take note of the Datenschutzhinweise gemäß Art. 13 Datenschutz-Grundverordnung (DSGVO) zur Erhebung und Verarbeitung von personenbezogenen Daten im Rahmen Ihrer Bewerbung. (data protection information on collecting and processing personal data contained in your application in accordance with Art. 13 of the General Data Protection Regulation (GDPR)). By submitting your application, you confirm that you have acknowledged the above data protection information of TUM.

Kontakt: n.kamal@tum.de

https://www.mls.ls.tum.de/en/cpb/home/