Postdoctoral Research Associate in Machine Learning for Mental Health

Voices for Voices; Greater London; On-site; GBP 32,000 - 42,000; Full time

Job summary

Careerwebsite is offering a two-year postdoctoral research position funded by UKRI for the project PROSPECT: Predicting psychosis outcomes from speech and brain connectivity. The role focuses on developing machine learning methods to predict longitudinal symptom changes in psychotic illnesses using MRI-derived brain networks.

The successful candidate will work at the intersection of machine learning, neuroimaging, and psychiatry, handling rich MRI datasets, processing data, and deriving network

Qualifications

• PhD or near completion in a relevant subject area (e.g., CS, Engineering, Physics, Mathematics, Psychiatry, Psychology, Neuroscience).
• Experience working with brain MRI data.
• Excellent computational skills with ML libraries in Python.
• Experience writing up research results for publication.
• Ability to communicate technical results to researchers at seminars/conferences.

Responsibilities

• Develop innovative ML approaches to predict psychosis outcomes from MRI data.
• Work at the intersection of ML, neuroimaging and Psychiatry to advance prediction accuracy.
• Curate and process rich brain MRI datasets and derive functional/structural networks.
• Explore uncertainty-aware ML methods and new definitions of longitudinal outcomes.

Skills

PhD or near completion

Brain MRI data experience

Machine learning in Python

Publish research results

Communicate technical results

Time management

Independent work

Team collaboration

Continuous professional development

Education

PhD or near completion in a relevant field

Tools

Python (scikit-learn)

PyTorch

TensorFlow

Job description

This is a 2 year postdoctoral research post, with the possibility of extension to 4 years, working on the UKRI funded project ‘PROSPECT: Predicting psychosis outcomes from speech and brain connectivity’. The overall aim of the role is to develop innovative machine learning approaches to predict longitudinal symptom changes for patients with psychotic illnesses, using patterns of brain connectivity derived from MRI.

The post holder will work at the intersection of machine learning, neuroimaging and Psychiatry, developing methods with the potential to improve our ability to predict clinical outcomes. They will have the opportunity to work with rich brain MRI datasets from patients with psychotic illnesses, curate and process these datasets, and derive both functional and structural brain networks. This will include using our group’s new Morphometric Inverse Divergence (MIND) approach for estimating structural similarity networks, which enables robust structural brain networks to be derived from T1-weighted images alone and has already been shown to be sensitive to schizophrenia.

A key focus of the project will be on testing novel approaches to improve the accuracy of psychosis outcome prediction from structural and functional brain networks. To that end, we will explore uncertainty-aware machine learning approaches, to assess whether prediction accuracy can be improved by focussing on particular subsets of patients. We will also investigate different ways to define longitudinal outcomes, and test whether combining functional and structural networks in novel ways can help improve prediction accuracy. For example, we will explore whether functional networks generated from structural brain networks can be used to capture additional predictive power. If the role holder desires, there may also be scope to relate brain imaging to speech and language data.

To be successful in this role, we are looking for candidates to have the following skills and experience:

Essential criteria

• PhD (or near completion) in a relevant subject area (including Computer Science, Engineering, Physics, Mathematics, Psychiatry, Psychology or Neuroscience. Note that this list is not exhaustive- candidates from other relevant backgrounds are welcome to apply);
• Experience working with brain MRI data;
• Excellent computational skills, including experience with Machine Learning (e.g. sklearn, pytorch, tensorflow in Python), and the ability to learn new computational techniques quickly as required;
• Experience writing up research results for publication;
• Able to communicate technical results to colleagues, and other researchers at seminars/conferences;
• Strong organisational and time management skills;
• Able to work independently, using initiative and creativity to explore own research directions;
• Able to work collaboratively as part of a cohesive team;
• Continuously updates knowledge in the specialist area and engages in continuous professional development.

Desirable criteria

• Experience of network analysis/graph theoretical techniques;
• Experience working with data related to mental health.