Computational Neuroscientist

Aurat is developing neuroscience-inspired AI models that predict how different human populations respond to scent. Our goal is to model the underlying neural and cognitive drivers of perception, emotion, and behavior from scent, enabling consumer insights without relying solely on traditional human panel testing. We are looking for a computational neuroscientist to help develop the next generation of our neural modeling platform. Responsibilities - Design and implement neuroscience-inspired machine learning models. - Develop neural architectures that predict population-specific responses to olfactory stimuli. - Build, train, and evaluate deep learning models using human perceptual datasets. - Design and execute experiments to improve model performance and generalization. - Explore novel modeling approaches, representation learning, and uncertainty estimation. - Work independently on open-ended research problems with minimal supervision. - Clearly communicate modeling decisions, assumptions, and experimental results. Required Qualifications - Computational neuroscience - Cognitive modeling - Strong background in machine learning and deep learning. - Strong Python programming skills. Preferred Qualifications Experience in one or more of the following areas: - Scientific machine learning - Representation learning - Bayesian modeling - Statistical learning - Brain-inspired AI - Sensory neuroscience - Psychophysics - Graph neural networks Ideal Candidate We're looking for someone who enjoys tackling novel scientific problems where there is no established solution. The ideal candidate is highly independent, comfortable reading scientific literature, capable of proposing new modeling approaches, and excited to rapidly iterate on experimental ideas. Contract Details - Remote - Full-time for two weeks - Immediate start - Potential for continued collaboration based on performance DO NOT APPLY IF YOU HAVE NO NEUROSCIENCE EXPERIENCE