Mass General Physicians Organization
Postdoc/Research Fellow AI-Driven Computational Biology for CNS Drug Discovery a
Mass General Physicians Organization, Boston, Massachusetts, us, 02298
We are seeking a highly motivated Postdoctoral Fellow to lead computational efforts focused on AI-accelerated drug discovery and delivery for neurological diseases. The ideal candidate will develop and apply cutting-edge machine learning methods to integrate multimodal and multiomics datasets, identify therapeutic targets/drugs, design novel compounds, and leverage our unique in vitro models and in vivo nanotechnology tools. This role will drive high-impact, interdisciplinary research at the intersection of AI, single-cell omics, and translational neuroscience, with access to state-of-the-art resources and close collaboration with experimental teams generating novel datasets. Join our dynamic, multidisciplinary team as we develop new strategies to tackle neurodegeneration, together with the incredible collaborators and resources of the MGB/HMS/Broad/MIT ecosystem and partner biopharmaceutical companies.
Responsibilities
Integrate multimodal data and develop and train AI foundation models on large-scale biological datasets for predictive modeling of cellular responses, target identification, and drug candidate identification.
Apply generative and sequence-based ML models to design, model, and optimize binding peptides.
Analyze high-throughput screens and biological datasets to inform in silico predictions.
Required Qualifications
PhD in Computational Biology, Bioinformatics, Machine Learning, Computer Science, Statistics, or a related field (recently completed or near completion).
Strong publication record demonstrating expertise in ML applied to biology.
Expertise in Python; experience with deep learning frameworks (PyTorch/TensorFlow); single-cell genomics analysis (Seurat, Scanpy, scVI, etc.); and perturbation modeling (e.g., GEARS, scGen).
Excellent organizational, communication, and teamwork abilities in a fast-paced research environment.
Interest in translational impact.
Preferred Qualifications
Experience with biological foundation models (e.g., scGPT, Geneformer, scBERT); generative AI for sequences/proteins/peptides (e.g., RFdiffusion variants); cheminformatics tools (e.g., RDKit, PubChem querying); active learning/uncertainty quantification; pipeline automation (e.g., custom scripts or Airflow); integration of public resources (e.g., OpenTargets, ChEMBL); or basic computational ADMET/pharmacokinetics assessment.
Background in neurodegenerative disease.
Wet lab skills in molecular and cellular biology, nanotechnology, and pharmacology.
How to Apply Please submit your CV, a cover letter detailing your relevant experience and interest in the project and contact information for three references to Alice Stanton at aestanton@mgh.harvard.edu. We look forward to having you join us in advancing the fight against neurological diseases!
Massachusetts General Physicians Organization, Inc. is an Equal Opportunity Employer. By embracing diverse skills, perspectives and ideas, we choose to lead. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, national origin, sex, age, gender identity, disability, sexual orientation, military service, genetic information, and/or other status protected under law. We will ensure that all individuals with a disability are provided a reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment.
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Responsibilities
Integrate multimodal data and develop and train AI foundation models on large-scale biological datasets for predictive modeling of cellular responses, target identification, and drug candidate identification.
Apply generative and sequence-based ML models to design, model, and optimize binding peptides.
Analyze high-throughput screens and biological datasets to inform in silico predictions.
Required Qualifications
PhD in Computational Biology, Bioinformatics, Machine Learning, Computer Science, Statistics, or a related field (recently completed or near completion).
Strong publication record demonstrating expertise in ML applied to biology.
Expertise in Python; experience with deep learning frameworks (PyTorch/TensorFlow); single-cell genomics analysis (Seurat, Scanpy, scVI, etc.); and perturbation modeling (e.g., GEARS, scGen).
Excellent organizational, communication, and teamwork abilities in a fast-paced research environment.
Interest in translational impact.
Preferred Qualifications
Experience with biological foundation models (e.g., scGPT, Geneformer, scBERT); generative AI for sequences/proteins/peptides (e.g., RFdiffusion variants); cheminformatics tools (e.g., RDKit, PubChem querying); active learning/uncertainty quantification; pipeline automation (e.g., custom scripts or Airflow); integration of public resources (e.g., OpenTargets, ChEMBL); or basic computational ADMET/pharmacokinetics assessment.
Background in neurodegenerative disease.
Wet lab skills in molecular and cellular biology, nanotechnology, and pharmacology.
How to Apply Please submit your CV, a cover letter detailing your relevant experience and interest in the project and contact information for three references to Alice Stanton at aestanton@mgh.harvard.edu. We look forward to having you join us in advancing the fight against neurological diseases!
Massachusetts General Physicians Organization, Inc. is an Equal Opportunity Employer. By embracing diverse skills, perspectives and ideas, we choose to lead. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, national origin, sex, age, gender identity, disability, sexual orientation, military service, genetic information, and/or other status protected under law. We will ensure that all individuals with a disability are provided a reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment.
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