Postdoctoral Fellow-MSH-13400-372

Department: Psychiatry

 

Physical work location: 1255 Fifth Avenue, Suite C1 New York, NY 10029

 

Name PI or Supervisor:  Dr. Sophia Frangou

 

Web link to Lab: n/a

 

Web link to Department: https://icahn.mssm.edu/about/departments-offices/psychiatry

 

Details of Research Project: 

 

CentileBrain is a neuroinformatics project that develops normative models and centile-based benchmarks for brain measures across the lifespan. The project integrates large-scale neuroimaging datasets, advanced statistical modelling, machine learning and reproducible computational workflows to support individual-level and group-level interpretation of brain structure and connectivity

 

 

Technical Duties: (include any protocols)

 

The post-doctoral fellow is expected to perform the following tasks: 

• Work with large multi-site neuroimaging datasets. 
• Follow institutional policies for data governance, privacy, human-subjects research and secure management of research data. 
• Conduct preprocessing and quality control of neuroimaging data. 
• Extract, harmonize and manage imaging-derived measures across datasets. 
• Prepare and maintain data dictionaries and other project materials. 
• Implement normative modelling, machine-learning and artificial intelligence pipelines. 
• Conduct statistical analyses in relation to project aims. 
• Prepare reproducible code, analytic workflows and clear technical documentation. 
• Support collaborative analyses with internal and external research partners. 
• Contribute to scientific manuscripts, conference presentations and grant reports. 

Educational and other Requirements for the position:

 

• PhD or MD/PhD in artificial intelligence, data science, machine learning, biomedical engineering, computer science, computational neuroscience, neuroimaging, biostatistics or a closely related quantitative field. 
• Strong formal training in artificial intelligence, data science and machine-learning methods. 
• Demonstrated ability to apply advanced computational methods to large-scale biomedical, neuroscience or neuroimaging data. 
• Strong quantitative and analytical skills, with the ability to develop, evaluate and interpret computational models. 
• Ability to work independently and collaboratively within a multidisciplinary research environment involving neuroscience, psychiatry, engineering, data science and clinical research. 
• Strong written and verbal communication skills, including the ability to communicate technical methods and findings to scientific collaborators. 

 

 

Experience Required:

 

The ideal candidate will have 

• Experience with machine-learning methods. 
• Experience with statistical modelling. 
• Strong scientific programming skills. 
• Experience developing and using reproducible computational workflows. 
• Experience with neuroimaging data analysis is strongly preferred. 
• Hands-on experience with MRI-based neuroimaging analysis, preferably including structural MRI, diffusion MRI, brain morphometry, connectivity measures or related imaging-derived phenotypes. 
• Experience with large-scale or multi-site datasets. 
• Experience with data harmonisation, normative modelling, artificial intelligence, high-performance computing, Git-based version control and reproducible research practices is highly desirable. 
• Experience with common neuroimaging tools and standards, such as FreeSurfer, FSL, ANTs, BIDS, fMRIPrep, Nipype or related platforms, would be advantageous. 
• A prior publication record in neuroimaging, computational neuroscience, data science, machine learning, artificial intelligence or biomedical engineering is preferred. 

 

 

Goals/Outcomes of the Research Project: 

The main goal of the project is to advance CentileBrain as a robust, reproducible and scalable platform for normative modelling of brain measures. Expected outcomes include harmonised neuroimaging datasets, validated normative models, individual-level centile and deviation outputs, documented computational pipelines, peer-reviewed manuscripts, conference presentations and open or shareable research tools where appropriate.

 

, 859 - Psychiatry - ISM, Icahn School of Medicine