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Research Assistant/Research Associate: The connectome of olfactory memory circuits in the adult fruit fly


One research assistant or associate post is available in the recently established Drosophila connectomics group headed by Greg Jefferis and Matthias Landgraf. The position is funded by a £3.25M Wellcome Trust collaborative award to the Department of Zoology. Preferred start date is 25th February 2019 or as soon as possible thereafter.

We are looking for a computational neurobiologist to strengthen the analysis of the very large amount of morphological and circuit data already generated through manual tracing in our project by contributing to current pipelines. In addition, we are looking for someone with the expertise to develop new ways to integrate and analyse semi-automated reconstruction data into our current workflow.

You must be a highly motivated individual with a strong quantitative background and research experience in a relevant area such as image analysis, network analysis, or neuroscience. You must have significant programming experience in scripting languages such as R and/or Python and ideally also high performance languages (eg C++ or Java) and databases (e.g. PostgreSQL). Demonstrated ability to write clean and well documented code e.g. through open source contributions is highly desirable. You must also have a serious interest in understanding the circuit basis of brain function and behavior.

The position will involve collaborative work across the project PIs, but will be particularly closely linked to the research group of Greg Jefferis at the MRC LMB which has developed a number of computational neuroanatomy tools. We will work to develop your research skills and scientific independence and will be happy to mentor individuals from different backgrounds; senior scientists will introduce you to all of our existing software infrastructure. You will also be expected to contribute directly to scientific publications and to present your work at UK and international conferences.

There will also be opportunities to mentor junior team members.

This is an opportunity to develop key analytic approaches to leverage large scale connectomics data in a model system that is having a major impact on circuit neuroscience.

Essential

  • For associate position: relevant PhD in neuroscience, computer science, physical sciences
  • For assistant position: relevant Masters or 2 or more years of practical experience in neuroscience, computer science, physical sciences
  • Significant experience with computer programming / scripting / data analysis (e.g. R, Python, Matlab, unix shell)
  • Basic understanding of neuroscience
  • Strong desire to understand circuit basis of brain function and behaviour
  • Experience analysing and writing scientific results;
  • Experience sharing code and data post-publication, following standard open research practices
  • Good communication skills (written and oral);
  • Ability to work in a team
  • Attention to detail

Desirable

  • Background in computational neuroanatomy
  • Proven ability to work with very large datasets
  • Ability to reason about computational bottlenecks
  • Proven ability to develop R or python packages
  • Ability to parse and write C++ code
  • Expertise in Drosophila neuroanatomy
  • Expertise in CATMAID EM reconstruction software or EM of neural circuits
  • Experience with Image Processing (e.g. Fiji / ImageJ)
  • Experience mentoring junior scientists

Duties and responsibilities

  • Contribute to current morphological and connectivity analysis pipelines
  • Develop new ways to integrate and analyse semi-automated reconstruction data
  • Presenting and writing up the results
  • Facilitating data sharing with public resources
  • Providing analytical skills expertise to the group
  • Mentoring junior team members

Interview dates: 4th to 15th February

Fixed-term: The funds for this post are available for 12 months in the first instance with a possibility of extension until 30 Sep 2020 subject to funding.

To apply online for this vacancy, please click on the 'Apply' button below. This will route you to the University's Web Recruitment System, where you will need to register an account (if you have not already) and log in before completing the online application form.

Please quote reference PF17766 on your application and in any correspondence about this vacancy.

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Further information

Apply online