The Stanton Lab at Harvard Medical School and Massachusetts General Hospital is recruiting a postdoctoral fellow for a cutting-edge position in AI-Driven Computational Biology for CNS Drug Discovery and Delivery. This opportunity is ideal for candidates passionate about leveraging advanced computational and machine learning methods to accelerate drug discovery for neurological diseases. The lab focuses on integrating multimodal and multiomics datasets, developing AI foundation models, and applying generative and sequence-based ML models to design, model, and optimize binding peptides. The successful candidate will work closely with a dynamic wetlab team and utilize state-of-the-art in vitro models and nanotechnology tools to validate predictions and drive translational neuroscience research. Applicants should hold a PhD in Computational Biology, Bioinformatics, Machine Learning, Computer Science, Statistics, or a related field, with a strong publication record in ML applied to biology. Required skills include Python programming, deep learning frameworks (PyTorch/TensorFlow), single-cell genomics analysis (Seurat, Scanpy, scVI), and perturbation modeling (GEARS, scGen). Preferred qualifications include experience with biological foundation models, generative AI for biological sequences, cheminformatics tools, active learning, pipeline automation, public resources, ADMET/pharmacokinetics, neurodegenerative disease background, and wet lab skills in molecular/cellular biology, nanotechnology, and pharmacology. The Stanton Lab offers access to the resources of Harvard Medical School, Massachusetts General Hospital, and the broader MGB/HMS/Broad/MIT ecosystem, providing a collaborative environment for high-impact interdisciplinary research. Funding details are not specified, but postdoctoral positions at these institutions typically include competitive salary and benefits. Interested candidates should submit a CV, cover letter, and contact information for three references to [email protected]. For more information, visit stantonlab.info .

The Stanton Lab at Harvard Medical School and Massachusetts General Hospital is seeking a dynamic postdoctoral researcher to lead an innovative project focused on in vitro 3D brain modeling and AI-coupling for ALS therapeutic discovery. The lab is part of a highly collaborative ecosystem, leveraging resources from MGB, HMS, and the Broad Institute, and is dedicated to developing new strategies to tackle neurodegeneration. The successful candidate will join a multidisciplinary team working on next-generation 3D multicellular brain models, multi-omics analysis, drug testing, and AI-coupling. The research environment includes collaborations with neuroscientists, geneticists, bioengineers, computational biologists, and physician-scientists, as well as leading academic institutions and biopharmaceutical companies. Required qualifications include a PhD in Cell Biology, Neuroscience, Bioengineering, or a related field; expertise in iPSC culture, differentiation, and 3D co-cultures; proficiency in cellular/molecular techniques (scRNAseq, FACS, IHC, ELISA, PCR, confocal microscopy); experience in neurodegenerative disease modeling (ideally ALS); genetic engineering skills; strong analytical abilities for data interpretation and assay optimization; and excellent organizational, communication, and teamwork skills. Preferred qualifications include organ-on-chip development, high-content screening in organoids, drug screening, pharmacology, ADMET profiling, computational analysis of multi-omic data, experience with AI frameworks (NLP, GNN, RL), familiarity with ALS patient cell repositories, and knowledge of AI frameworks. Funding details are not specified, but the position is a postdoctoral research fellow role at Harvard Medical School and Massachusetts General Hospital, which typically includes a competitive salary and benefits. Interested candidates should submit their CV, a cover letter detailing relevant experience and interest in the project, and contact information for three references to [email protected]. For more information, visit the Stanton Lab website.