Postdoc: Machine Learning for Brain Networks & Psychosis

Kessler Foundation; Greater London; On-site; GBP 34,000 - 42,000; Full time

Job summary

Kessler Foundation in the United Kingdom hosts a two-year postdoctoral research position on the PROSPECT project, with extension potential to four years. The role focuses on creating machine learning methods to predict psychosis outcomes from MRI-derived brain networks.

The successful candidate will bridge machine learning, neuroimaging, and psychiatry, handling rich MRI datasets, and potentially relating imaging to speech and language data.

Qualifications

• PhD (or near completion) in a relevant subject area.
• Experience working with brain MRI data.
• Excellent computational skills with Python ML libraries.
• Experience writing up research results for publication.
• Able to communicate technical results at seminars/conferences.
• Strong organisational and time management skills.
• Able to work independently and explore own research directions.
• Able to work collaboratively in a team.
• Committed to continuous professional development.

Responsibilities

• Develop innovative machine learning approaches for predicting psychosis outcomes.
• Work with functional and structural brain networks from MRI data.
• Explore novel uncertainty-aware ML methods for predicting longitudinal outcomes.
• Investigate combining functional and structural networks for improved prediction.

Skills

Brain MRI data

Python ML libraries

Machine Learning

Research publication writing

Communication of technical results

Time management

Independent research

Team collaboration

Continuous professional development

Education

PhD (or near completion)

Tools

scikit-learn

PyTorch

TensorFlow

Job description

Kessler Foundation in the United Kingdom hosts a two-year postdoctoral research position on the PROSPECT project, with extension potential to four years. The role focuses on creating machine learning methods to predict psychosis outcomes from MRI-derived brain networks.

The successful candidate will bridge machine learning, neuroimaging, and psychiatry, handling rich MRI datasets, and potentially relating imaging to speech and language data.