Research Assistant in Computational Neuroscience

University of Cambridge Department of Engineering

United Kingdom

This is an opportunity for a highly creative and skilled pre-doctoral Research Assistant to join the dynamic and multidisciplinary research environment of the Computational and Biological Learning research group (https://www.cbl-cambridge.org/), Department of Engineering, University of Cambridge. We are looking for a Research Assistant to work on projects related to statistical learning and contextual inference in the human brain. We have a particular focus of learning of aversive states, as this has a strong clinical significance for chronic pain and mental health disorders. The RA will be supervised by Dr Flavia Mancini (MRC Career Development fellow, and Head of the Nox Lab www.noxlab.org), and is expected to collaborate with theoretical and experimental colleagues in Cambridge, Oxford and abroad. The post holder will be located in central Cambridge, Cambridgeshire, UK.

As a general approach, we combine statistical learning tasks in humans, computational modelling (using Bayesian inference, reinforcement learning, deep learning and neural networks) with neuroimaging methods (including 7T fMRI). The successful candidate will strengthen this approach and be responsible for designing experiments, collecting and analysis behavioural and brain fMRI data using computational modelling techniques.

The key responsibilities and duties are:

  • Ideating and conducting research studies on statistical/aversive learning, combining behavioural tasks, computational modelling (using Bayesian inference, reinforcement learning, deep learning and/or neural networks) with fMRI in healthy volunteers and chronic pain patients.
  • Disseminating research findings
  • Maintaining and developing technical skills to expand their scientific potential

The skills, qualifications and experience required to perform the role are:

  • Obtained (or be close to obtaining) a MD in computational or cognitive neuroscience, physics, mathematics, machine learning, medicine, biology, psychology or a related neuroscience field.
  • A quantitative background, with research experience in theoretical and/or cognitive/behavioural neuroscience.
  • Demonstrable programming skills/experience, able to run simulations and modelling of behavioural/neural data using Bayesian, reinforcement learning and/or deep learning techniques.
  • Specialist skills in neuroimaging methods, from data collection to analysis are desirable (ideally fMRI, but EEG/MEG, EcoG are also relevant to the post).
  • Good organizational and project management skills
  • Ability to work both independently and as part of a team
  • Excellent communication skills

Salary Ranges: £27,116 - £31,406

Fixed-term: The funds for this post are available for 12 months in the first instance.

Click the 'Apply' button below to register an account with our recruitment system (if you have not already) and apply online.

Please ensure that you upload in the Upload section of the online application:

  • your Curriculum Vitae (CV)
  • a covering letter
  • your academic transcripts
  • a research statement discussing your previous research and your interests for future work.

If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application. Please submit your application by midnight on the closing date.

If you have any questions about this vacancy, please contact Dr Flavia Mancini (flavia.mancini@eng.cam.ac.uk) for queries of a technical nature related to the role and cbl-enquiries@eng.cam.ac.uk for queries related to the application process.

Please quote reference NM32163 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.


In your application, please refer to Professorpositions.com

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