Posting Description
POSTDOCTORAL ASSOCIATE DORKENWALD LAB McGovern Institute for Brain Research - Dorkenwald laboratory to develop computational reconstruction analysis and modeling approaches for connectomics datasets. The lab develops computational approaches to reconstruct analyze and model large-scale connectomes aiming to uncover organizational principles of neuronal circuits and how circuit structure supports computation. Will lead research on one or more of the following areas: Automated proofreading & annotation at scale: Machine learning approaches for error detection human-in-the-loop proofreading of automated cell reconstructions active-learning approaches for efficient annotation and self-supervision approaches for tokenizing image datasets and cell reconstructions; Circuit analysis & modeling: Analysis of cortical connectomes including comparative analyses across ages/regions; hypothesis-driven tests of discovered circuit rules; pair analyses with data-constrained models (e.g. RNNs dynamical systems) and simulations; Morphology representation & multi-modal linking: Learn representations of detailed cell morphologies to link across datasets (within connectomics) and across modalities (e.g. EM Patch-seq) to build multi-modal connectomic resources that provide the basis for analyses that combine e.g. connectivity with transcriptomic information; and publish in leading venues maintain high-quality reproducible code collaborate across McGovern/BCS and external collaborators and mentor students.
Job Requirements
REQUIRED: Ph.D. in neuroscience computer science or a related field; experience in machine learning and developing and validating computational analysis workflows; attention to detail; excellent interpersonal analytical problem-solving organizational documentation communication and time-management skills; self-motivation and ability to work independently; and ability to work as part of a tightly knit team.
10/6/2025
Required Experience:
IC
The MIT Media Lab is an interdisciplinary research lab that encourages the unconventional mixing and matching of seemingly disparate research areas.