Massachusetts Institute of Technology
Postdoctoral Associate, Dorkenwald Lab
Skills
8 months ago
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Important information
Contract type:
Permanent
Salary:
$73,308 – $90,000 / year
Location:
Cambridge, MA, USA
Work mode:
Onsite
Published on:
7 October 2026
What they need
Working at MIT offers opportunities that just aren’t found anywhere else, including generous and unique benefits that help to ensure that MIT employees are healthy, supported, and enjoy a fulfilling work/life balance.
We welcome people from all walks of life to bring their talent, ideas, and experience to our community. We strongly encourage applications from individuals from all identities and backgrounds – like yours. If you want to be part of our exceptional, multicultural, collaborative, and inclusive community, then take a look at this opportunity.
MIT provides pay ranges representing its good faith estimate of what the Institute reasonably expects to pay for a full time position at the time of posting (if you are applying for a part time salaried job, you will need to prorate the posted pay range). The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience and education/training, internal peer equity, and applicable legal requirements. This pay range represents base pay only and does not include any other benefits or compensation.
Postdoctoral Associate, Dorkenwald Lab
Job Number: 25250
Functional Area: Scientific Computing
Department: McGovern Institute for Brain Research
School Area: Office of Provost
Pay Range Minimum: $73,308
Pay Range Maximum: $90,000
Employment Type: Full-Time
Employment Category: Exempt
Visa Sponsorship Available: Yes
Schedule:
Pay Grade: No Grade
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
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