Postdoctoral Research Fellow in AI/ML for Multimodal Prognostication
Postdoctoral Research Fellow in AI/ML for Multimodal Prognostication
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Job Description
Mass General Brigham relies on a wide range of professionals including doctors nurses business people tech experts researchers and systems analysts to advance our mission. As a not-for-profit we support patient care research teaching and community service striving to provide exceptional care. We believe that high-performing teams drive groundbreaking medical discoveries and invite all applicants to join us and experience what it means to be part of Mass General Brigham.
Job Summary
How To Apply:To apply please submit your CV and a letter of interest to Morteza Zabihi at
Interested candidates should submit a single PDF file including:
When submitting your application please ensure the email subject line follows this format: Postdoc Application Your Full Name
-page CV detailing relevant experience and publications.
-page cover letter with exactly five bullet points each no more than two lines demonstrating your fit for this position.
information for three references.
Join us in advancing AI-driven precision medicine and neurological prognostication!
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We at the Mass General Brigham NeuroAI Center are seeking a highly motivated Postdoctoral Research Fellow with expertise in machine learning (ML) and biomedical data analysis to contribute to cutting-edge research at the intersection of neuroscience critical care and computational modeling. This position will focus on developing advanced AI/ML models for prediction and outcome assessment in acute neurological conditions leveraging EEG EHR telemetry and neuroimaging data.
Key Responsibilities:
Develop and validate multimodal AI/ML models integrating diverse clinical and physiological data.
Design and implement time-series prediction frameworks utilizing transformer-based architectures ensemble learning and deep learning techniques.
Manage large-scale electronic health record (EHR) EEG and telemetry datasets ensuring robust preprocessing feature extraction and handling of missing data.
Apply explainable AI (XAI) techniques such as SHAP and attention mechanisms to enhance model interpretability.
Implement validation strategies including nested cross-validation conformal prediction for uncertainty quantification and adversarial training for model robustness.
Collaborate with a multidisciplinary team of clinicians data scientists and engineers to refine models for real-world deployment.
Contribute to manuscript preparation grant writing and dissemination of research findings at leading conferences and journals.
Qualifications:
Ph.D. in computer science biomedical engineering computational neuroscience applied mathematics or a related field.
Strong expertise in machine learning deep learning and statistical modeling with applications in biomedical data.
Experience with time-series analysis transformers LSTMs and other temporal modeling techniques.
Proficiency in Python PyTorch and ML frameworks; experience with EHR data processing and feature engineering is a plus.
Familiarity with neurophysiological data (EEG telemetry) and neuroimaging analysis is highly desirable.
Strong publication record in AI/ML applications for healthcare or neuroscience.
Excellent problem-solving skills ability to work independently and strong collaborative mindset.
Excellent written and oral communication skills
Preferred Skills (Not Required but a Plus):
Proven ability to efficiently utilize cloud computing platforms (e.g. Azure AWS Google Cloud) and high-performance computing (HPC) clusters for scheduling assigning and managing computational research jobs.
Knowledge of self-supervised learning and domain adaptation.
Familiarity with neuroscience-related ML challenges such as predicting clinical deterioration or integrating multimodal physiological data.
What We Offer:
A dynamic interdisciplinary research environment at the forefront of AI in neuroscience and critical care.
Access to large-scale clinical datasets and state-of-the-art computational resources.
Opportunities to publish in top-tier journals and present at leading conferences.
A collaborative and intellectually stimulating research team with strong clinical and computational expertise.
Qualifications
How To Apply:
To apply please submit your CV and a letter of interest to Morteza Zabihi at
Interested candidates should submit a single PDF file including:
When submitting your application please ensure the email subject line follows this format: Postdoc Application Your Full Name
Two-page CV detailing relevant experience and publications. One-page cover letter with exactly five bullet points each no more than two lines demonstrating your fit for this position. Contact information for three references.
Join us in advancing AI-driven precision medicine and neurological prognostication!
----
We at the Mass General Brigham NeuroAI Center are seeking a highly motivated Postdoctoral Research Fellow with expertise in machine learning (ML) and biomedical data analysis to contribute to cutting-edge research at the intersection of neuroscience critical care and computational modeling. This position will focus on developing advanced AI/ML models for prediction and outcome assessment in acute neurological conditions leveraging EEG EHR telemetry and neuroimaging data.
Key Responsibilities:
- Develop and validate multimodal AI/ML models integrating diverse clinical and physiological data.
- Design and implement time-series prediction frameworks utilizing transformer-based architectures ensemble learning and deep learning techniques.
- Manage large-scale electronic health record (EHR) EEG and telemetry datasets ensuring robust preprocessing feature extraction and handling of missing data.
- Apply explainable AI (XAI) techniques such as SHAP and attention mechanisms to enhance model interpretability.
- Implement validation strategies including nested cross-validation conformal prediction for uncertainty quantification and adversarial training for model robustness.
- Collaborate with a multidisciplinary team of clinicians data scientists and engineers to refine models for real-world deployment.
- Contribute to manuscript preparation grant writing and dissemination of research findings at leading conferences and journals.
Qualifications:
- Ph.D. in computer science biomedical engineering computational neuroscience applied mathematics or a related field.
- Strong expertise in machine learning deep learning and statistical modeling with applications in biomedical data.
- Experience with time-series analysis transformers LSTMs and other temporal modeling techniques.
- Proficiency in Python PyTorch and ML frameworks; experience with EHR data processing and feature engineering is a plus.
- Familiarity with neurophysiological data (EEG telemetry) and neuroimaging analysis is highly desirable.
- Strong publication record in AI/ML applications for healthcare or neuroscience.
- Excellent problem-solving skills ability to work independently and strong collaborative mindset.
- Excellent written and oral communication skills
Preferred Skills (Not Required but a Plus):
- Proven ability to efficiently utilize cloud computing platforms (e.g. Azure AWS Google Cloud) and high-performance computing (HPC) clusters for scheduling assigning and managing computational research jobs.
- Knowledge of self-supervised learning and domain adaptation.
- Familiarity with neuroscience-related ML challenges such as predicting clinical deterioration or integrating multimodal physiological data.
What We Offer:
- A dynamic interdisciplinary research environment at the forefront of AI in neuroscience and critical care.
- Access to large-scale clinical datasets and state-of-the-art computational resources.
- Opportunities to publish in top-tier journals and present at leading conferences.
- A collaborative and intellectually stimulating research team with strong clinical and computational expertise.
Additional Job Details (if applicable)
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EEO Statement:
Mass General Brigham Competency Framework
At Mass General Brigham our competency framework defines what effective leadership looks like by specifying which behaviors are most critical for successful performance at each job level. The framework is comprised of ten competencies (half People-Focused half Performance-Focused) and are defined by observable and measurable skills and behaviors that contribute to workplace effectiveness and career success. These competencies are used to evaluate performance make hiring decisions identify development needs mobilize employees across our system and establish a strong talent pipeline.
Employment Type
Grant
