Address
Form of work
SalaryGrade UE07: £37,099 to £44,263 per annum (A revised salary range for this grade of £39,347 to £46,974 is planned to take effect from Spring 2024)
College of Science and Engineering / School of Informatics
Contract type: Full-time (35 hours per week)
Fixed-term: until 31 July 2026
The Opportunity:
Technologies to record the electrical activity from many neurons, across multiple brain areas and during behaviour, are rapidly advancing and pose a formidable challenge to data analysis and interpretation. We are seeking a highly motivated and skilled Machine Learning Postdoctoral Researcher to join our team to develop advanced methodologies for analysis of modern, large-scale extracellular recordings. This project aims to address key challenges in the field, including the lack of automation, standardisation, and interoperability in existing computational tools. You will develop new methods for the fully automated and reliable extraction of single neuron activity from extracellular recordings and implement these methods in the well-established SpikeInterface framework (https://github.com/SpikeInterface/spikeinterface). The project is highly collaborative and offers opportunities to interact and join forces with experimental labs using state-of-the-art recording devices, and with researchers working on related analysis methods. This project is a collaboration between the labs of Matthias Hennig (School of Informatics) and Matthew Nolan (Centre for Discovery Brain Sciences, Edinburgh).
This post is full-time (35 hours per week), however, we are open to considering part-time or flexible working patterns. We are also open to considering requests for hybrid working (on a non-contractual basis) that combines a mix of remote and regular on-campus working.
Your skills and attributes for success:
A Ph.D. (or equivalent experience) in Machine Learning, Computer Science, Mathematics, Physics, Engineering, Computational Neuroscience, or a related field.
A strong background in machine learning, signal processing, and algorithm development. Previous experience of working with biological data is of advantage but not essential.
Proficiency in Python is essential as the SpikeInterface framework is written in this programming language. Familiarity with relevant libraries and frameworks (e.g., numpy, scikit-learn, TensorFlow, PyTorch) is also required. Experience in software development, version control systems (e.g., Git) and software testing methodologies is desirable.
Effective communication and teamwork abilities, and the ability to communicate across disciplines.
