Postdoc: Machine Learning for Psychosis Outcome Prediction

Voices for Voices; Greater London; Hybrid; 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

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