To work within the group of Dr Albert Cardona at the MRC Laboratory of Molecular Biology (LMB), within a programme aimed at whole brain connectomics from volume electron microscopy. Specifically, we are seeking to recruit a data scientist with at least a year of experience with densely labelled volume electron microscopy data of nervous tissue. In particular, the candidate will be experienced in developing and applying machine learning frameworks for synapse detection and segmentation, neuron segmentation and proofreading, and quantification of neuronal structures in nanometre-resolution data sets imaged with volume electron microscopy, for the purpose of mapping neuronal wiring diagrams from volume electron microscopy. The ideal candidate will have an academic track record in the form of authored publications in the arXiv, computer vision conferences, and scientific journals, as well as accessible source code repositories demonstrating past work.

The ideal candidate will have experience with the python programming language (at version 3+), and in the use of machine learning libraries with python bindings such as keras or pytorch, and has written code available in accessible source code repositories where it can be evaluated by third parties, and has deployed their code to both CPU and GPU clusters, and single servers with multiple GPUs. The ideal candidate has applied all of the above towards the generation of over-segmentations of neuronal structures, and is familiar with approaches for post-processing (proofreading) to automatically agglomerate over-segmented neuron fragments into full arbors, using biologically grounded approaches such as microtobule or endoplasmatic reticulum segmentation for validation.