Postdoctoral Fellow-MSH-13400-372

Mount Sinai; United States; On-site; USD 85,000 - 120,000; Full time

Job summary

Mount Sinai's Psychiatry Department in New York seeks a post-doctoral fellow to advance CentileBrain, a normative brain-modeling platform, using AI and machine learning on large multi-site neuroimaging datasets.

The role requires PhD/MD-PhD in a quantitative field, strong data-science training, and the ability to develop reproducible computational workflows. The project emphasizes collaboration across neuroscience, psychiatry and engineering.

Qualifications

• 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.

Responsibilities

• 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.

Skills

Machine learning

Data science

Statistical modelling

Scientific programming

Reproducible workflows

Neuroimaging analysis

MRI analysis

Large-scale datasets

Education

PhD or MD/PhD in a quantitative field

Tools

FreeSurfer

FSL

ANTs

BIDS

fMRIPrep

Nipype

Job description

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