Research Assistant

**Job Description**
A Research Assistant position focused on investigating the functional roles and distribution patterns of plant-Mucoromycotina “fine root endophyte” (MFRE) symbioses, funded by the European Research Council. The project aims to address critical knowledge gaps by combining controlled laboratory experiments, field studies, and advanced microscopy to generate comprehensive datasets. The role involves developing and applying analytical frameworks to interpret these large datasets, enabling new insights into MFRE distribution and function, particularly their contribution to plant health, diversity, and ecosystem dynamics across various environments.

**Skills & Abilities**
• Demonstrable experience of research using molecular techniques.
• Experience of research using isotope tracers, particularly in multi-trophic plant-microbe interaction scenarios.
• Statistical or bioinformatic analysis and interpretation of large datasets related to plant-microbe interactions.
• Strong analytical skills.
• Ability to work well independently and as part of a team.
• Excellent time management skills.
• Effective communication skills, both written and verbal, including report writing and presentation delivery.
• Proficiency in building and validating statistical and bioinformatics methods in appropriate software packages.
• Experience of working as part of a collaborative team.
• Willingness to co-supervise UG/PGT students.
• Excellent record keeping and data management skills, with appropriate IT experience.
• Ability to analyse and solve problems with an appreciation of longer-term implications.
• Ability to assess and organise resources, and plan and progress work activities.
• Proven flexible approach with the ability to prioritise workloads.
• Theoretical and practical knowledge in mycorrhizal symbioses.

**Qualifications**
Required Degree(s) in:
• Biology
• Ecology
• Microbiology
• Plant Science
• Soil Science
• Related discipline

**Experience**
Other:
• Demonstrable experience of research using molecular techniques, isotope tracers, and statistical or bioinformatic analysis of large datasets related to plant-microbe interactions, particularly in multi-trophic scenarios.

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