University of Oregon, Institute of Neuroscience

Position ID:UO-Institute of Neuroscience-POSTDOC1 [#26102, 532695]
Position Title: Postdoc position in theoretical neuroscience and NeuroAI
Position Type:Postdoctoral
Position Location:Eugene, Oregon 97403, United States [map] sort by distance
Subject Areas: Theoretical Physics
AI/Machine Learning
Appl Deadline:2023/12/01 11:59PMhelp popup finished (2023/10/05, finished 2024/06/08, listed until 2024/04/05)
Position Description:   Remote  

*** this position has been closed. ***

Position Summary:

Postdoc positions are available for researchers with strong quantitative backgrounds at the University of Oregon’s NeuroAI Center ( in the Murray and Mazzucato labs. In our research, we seek to uncover the principles of how the brain performs computations related to sensory perception, decision making, and motor control. While previous experience in computational neuroscience and machine learning are desirable, applicants from other quantitative fields (e.g. math, physics, statistics, computer science) who are eager to learn about neuroscience are highly encouraged to apply as well.

Some of the scientific questions that motivate our work are: How do different regions of the brain interact to learn new motor skills? What are the neural mechanisms underlying optimal performance in complex cognitive tasks? What are the principles that enable brains and artificial agents to learn efficiently from experience while minimizing forgetting? How can neural circuits generate the complex dynamics enabling naturalistic animal behavior? Our labs aim at building mechanistic models of brain function grounded in a combination of theoretical approaches, neural network-based simulations, and statistical analysis of experimental data. The candidate will have the opportunity to collaborate with a large network of experimental collaborators at University of Oregon and at other institutions with expertise in sensory processing (visual, auditory, and olfactory), motor control, naturalistic behavior, neural engineering, and brain-computer interfaces.

The Oregon NeuroAI Center is part of the Institute of Neuroscience at the University of Oregon (, a major hub for systems and theoretical neuroscience research. The successful candidate will also join our International Network for Bio-Inspired Computing (, a worldwide consortium of NeuroAI groups that provides trainees with an extended network for collaboration, including trainee exchanges, workshops, and schools. The University of Oregon is located in Eugene, Oregon, a vibrant college town in the Pacific Northwest with ample cultural offerings and phenomenal access to outdoor recreation.

We offer a competitive salary commensurate with the candidate’s experience, and remote work arrangements may be considered. Our Institute strongly advocates for inclusivity in science, and we encourage applications from underrepresented groups.

Required Qualifications:

Successful candidates will have a PhD in a quantitative field, including physics, neuroscience, mathematics, statistics, computer science, or related fields. Applicants should have a strong quantitative background including at least some coding experience.


Application reviews will start on December 1st and will continue until the position is filled. Applications should include a CV, two-page statement of research interests, a one-page DEI statement (guidelines:, and contact information for three letters of reference.


- S Ogawa, F Fumarola, L Mazzucato (2023). Multitasking via baseline control in recurrent neural networks. Proceedings of the National Academy of Sciences, 120(33), e2304394120.

- JP Portes, C Schmid, JM Murray (2022). Distinguishing Learning Rules with Brain Machine Interfaces. Advances in Neural Information Processing Systems, 35, 25937.

- S Recanatesi, U Pereira-Obilinovic, M Murakami, Z Mainen, L Mazzucato (2022). Metastable attractors explain the variable timing of stable behavioral action sequences. Neuron, 110(1), 139-153.

- L Mazzucato (2022). Neural mechanisms underlying the temporal organization of naturalistic animal behavior. Elife, 11, e76577.

- JM Murray, GS Escola (2020). Remembrance of things practiced with fast and slow learning in cortical and subcortical pathways. Nature Communications, 11, 1.

We are not accepting applications for this job through AcademicJobsOnline.Org right now. Please apply at external link.
Contact: Luca Mazzucato and James Murray
Email: email address
Postal Mail:
1254 University of Oregon
222 Huestis Hall
Eugene, OR 97403-1254
Web Page: