Postdoctoral fellow in Biology Lund University, Faculty of Science, Department of Biology Sweden

Lund University, Faculty of Science, Department of Biology

 

Lund University was founded in 1666 and is repeatedly ranked among the world’s top 100 universities. The University has 40 000 students and 7 400 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition.

 

The Faculty of Science conducts research and education within Biology, Astronomy, Physics, Geosciences, Chemistry, Mathematics and Environmental Sciences. The Faculty is organized into nine departments, gathered in the northern campus area. The Faculty has approximately 1500 students, 330 PhD students and 700 employees.

 

Job description

In recent work our group has put forward a model for insect path integration that provides a detailed set of predictions for all neural elements required to implement this fundamental navigation strategy in the central complex of the bumblebee brain. One of the key elements, but also one of the most speculative, is the neural basis for the working memory that underlies the storage of the integrated foraging history of the bee. That is, the proposed neurons are suggested to integrate speed and directional information over time and therefore generate an activity based memory of the animal’s current position in relation to a point of origin. Probing the validity of these predictions is a major challenge and will, if successful, lead to the final identification of the neural correlate of the insect path integrator. Moreover, it will allow us to gain cellular and circuit based understanding of the neural basis of working memory in general.

 

The advertised, ERC funded, postdoctoral project will use molecular, behavioral, anatomical, and electrophysiological approaches to examine the neurons in the bumblebee central complex that have been proposed to house the path integration working memory. Bees will be trained to obtain a defined path integration memory. These full-vector bees will be tested for possible expression of genes that mark ongoing neural activity (immediate early genes) to locate and visualize a memory footprint resulting from path integration. As these methods are not yet established in the bumblebee central complex, developing a viable protocol will be a first major aim of the project. Secondly, intracellular and extracellular recordings from the proposed memory cells will also be performed with the aim of testing their sensory response features against our model predictions. These experiments will be performed both in naive as well as in trained bees. Extracellular recordings carried out in behaving bees (ideally while performing path integration) will be the final, long term goal of this part of the project.

 

A successful candidate will have a PhD in a relevant field (obtained within the last 3 years) and extensive training in insect molecular biology, in situ hybridization, histology and immunohistochemistry (see list below for other required skills). A verifiable ability to work independently, to pursue self-motivated initiatives, for acquiring new skills/methods efficiently and for creative problem solving will be essential, given the pioneering nature of the project. Publications in international, high-visibility journals that show the successful mastering of relevant techniques as well as original thinking, are highly desirable.

 

Required qualifications

  • PhD received less than 3 years before anticipated starting date
  • Experience of In situ hybridization on insect brain tissue
  • Experience in immunohistochemistry on insect brain tissue
  • Experience in confocal microscopy
  • Experience in histology techniques
  • General molecular Biology skills
  • Experience in image analysis
  • Very good writing and presentation skills, excellent command of the English language

 

Desirable skills

  • Experience with behavioral experiments in insects, preferably bees
  • Knowledge of antibody design and testing
  • Knowledge of electrophysiology
  • Programming (Matlab, Python)


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