Computational Neuroscience

Andrew Huberman joins Patrick O’Shaughnessy to discuss the frontier of neurotechnology and sustained human performance. Their conversation considers the convergence of AI and biotechnology, gene-based methods for changing neural activity and motivation, and the relationships between visual behaviour, arousal and sleep. Huberman is a neuroscientist and professor at Stanford University.

Neurobiologist Tom Clandinin joins Nicholas Weiler to explore how interacting genetic, cellular and metabolic systems maintain brain health over a lifetime, and how their breakdown contributes to neurodegeneration. The discussion moves beyond single explanations such as inflammation or waste clearance to a systems-level study of brain resilience. Clandinin describes a collaborative programme combining large-scale genetic perturbations in flies and mice with molecular, circuit and behavioural measurements. Computational models will connect these observations across cell types, predict interactions and guide experiments toward potential ways to strengthen the aging brain’s maintenance systems.

Gaute Einevoll and Mihai Petrovici discuss how neuromorphic hardware can support artificial intelligence and efficient brain simulation. They contrast high-frequency arithmetic on conventional silicon with the rich dynamics and millisecond spikes of biological neurons, and explore brain-inspired systems for emulating neural dynamics and studying computation in spiking networks. Petrovici is a senior researcher at the University of Bern. The conversation was recorded on 18 June 2026 and released on 23 September 2026; the publisher’s audio runs 1 hour, 41 minutes and 46 seconds.

Daniel Levenstein discusses spontaneous activity in the hippocampus and cortex, especially during sleep, and its relationship to learning, memory and navigation. He explains how AI models can help investigate these dynamics, alongside questions about model systems, cognitive maps and the relationship between neuroscience experiments and theory.

Lead research on distributed neural computation and brain evolution using the acoel Hofstenia miamia. The project combines circuit imaging, activity recordings, neural perturbations, behavioural analysis and computational methods to explain how repeated circuit units produce coordinated, adaptive behaviour. This is a full-time, four-year appointment in London, starting from £47,500 annually with benefits. The institute states that this role is eligible for UK visa sponsorship. Applications close on 11 October 2026 at 23:59, according to the current employer advert.

A 23-month postdoctoral appointment at 0.8 FTE in Pieter Medendorp’s sensorimotor research group at the Donders Institute. The project investigates perception, movement and decisions under uncertainty through behavioural experiments and computational models. Work combines multisensory integration, Bayesian modelling and optimal control with virtual reality, wearable sensors and motion platforms, including natural navigation, orienting and reaching. The preferred start is 1 December 2026. Responsibilities may include student supervision and teaching.

The BrAIN group recruits a postdoctoral researcher for the ERC-funded BrainAlign project, investigating whether human brain activity can improve language models’ long-timescale understanding. The researcher leads the computational neuroscience work, integrates heterogeneous fMRI datasets and develops models that learn from naturalistic language comprehension. The project also uses these models to investigate language processing in the brain. Candidates need a relevant PhD, strong computational or machine-learning skills and programming experience; fMRI expertise is particularly useful. Apply through the Max Planck Postdoc Program with a CV and publications, research summary, statement of interest and up to three referees. The project advert describes rolling review; the programme’s September 2026 application window closes on 13 October at 12:00 CEST.

Postdoctoral research with Christian Doeller at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. The group studies structured representations supporting navigation, episodic and working memory, learning and decision-making. Projects connect hippocampal population coding with knowledge representations and possible translation to Alzheimer’s disease. Approaches include high-field fMRI, MEG, EEG, virtual reality, psychophysics and computational models, including machine learning and deep neural networks. The project can be shaped around interdisciplinary interests. Applicants need a PhD in cognitive neuroscience, psychology, biology, computer science, physics or a related field, strong programming skills, an excellent academic record and interest or experience in computational modelling. The September 2026 Max Planck Postdoc Program offers at least three years of employment and structured career development. Apply through the programme portal by 13 October 2026 at 12:00 Europe/Berlin (CEST). Create an account to access Apply and submit the required CV with publications, previous-research summary and statement of interest, following the portal’s full instructions.

Neurobiologist Tom Clandinin joins Nicholas Weiler to explore how interacting genetic, cellular and metabolic systems maintain brain health over a lifetime, and how their breakdown contributes to neurodegeneration. The discussion moves beyond single explanations such as inflammation or waste clearance to a systems-level study of brain resilience. Clandinin describes a collaborative programme combining large-scale genetic perturbations in flies and mice with molecular, circuit and behavioural measurements. Computational models will connect these observations across cell types, predict interactions and guide experiments toward potential ways to strengthen the aging brain’s maintenance systems.

Andrew Huberman joins Patrick O’Shaughnessy to discuss the frontier of neurotechnology and sustained human performance. Their conversation considers the convergence of AI and biotechnology, gene-based methods for changing neural activity and motivation, and the relationships between visual behaviour, arousal and sleep. Huberman is a neuroscientist and professor at Stanford University.

Arno Onken examines transformer models for predicting responses in the retina and visual cortex. These models learn complex neural-response patterns from large datasets and can transfer learned representations between settings, but fitting and interpreting them differs from linear–nonlinear and convolutional approaches. The talk discusses the opportunities and difficulties this creates for understanding sensory processing. It includes ViV1T, a transformer trained on natural movies that revealed previously uncharacterised response properties in mouse primary visual cortex. The work illustrates how predictive models can generate hypotheses and guide animal experiments. Onken also considers the substantial data and computing requirements and the difficulty of interpreting what the models have learned.

Develop an independent research programme in computational neuroscience at UC Berkeley. The search spans neural-data analysis, circuit modelling, mathematical accounts of perception, memory, cognition and movement, theoretical neuroscience, machine learning for brain modelling, and biological intelligence. Collaboration with experimental laboratories is valued. The appointment includes research, external funding, teaching, mentoring and service. The anticipated start is 1 July 2027. The advertised nine-month salary is USD 84,100–132,900, with possible additional compensation. Applications close on 30 October 2026 at 23:59 Pacific Time.