The López-Schier laboratory is looking for PhD candidates to join a multidisciplinary research project that combines experimental and computational neuroscience. The aim of the project is to understand the neuronal bases of spatial navigation. The project is fully funded and part of a consortium of experimental and theoretical neuroscientists in Germany, France and the USA. We are looking for outstanding, highly motivated and ambitious candidates with a solid background in physics, engineering, computer science, or theoretical neuroscience, and a genuine interest in animal behaviour. The positions are fully funded with ideally start in March-June 2021. You will join a multidisciplinary team at the Helmholtz Zentrum in Neuherberg-Munich, Germany. A good command of the English language is necessary. Other requirements are computer programming skills, and good understanding machine learning and machine vision. The Helmholtz Zentrum München is world-renowned for its fundamental research and is among the top research institutions in the world. Munich is cosmopolitan city with a lively lifestyle and outstanding outdoors. Candidates must send their application including a brief letter of interest, a complete CV, as well as contact information of two or three academic references to Dr. Hernan Lopez-Schier

Research Fellow position: This project explores individuality of neural circuits and neural activity in C. elegans brain, based on whole-brain-activity data and information about the C. elegans connectome (neural circuit wiring data). The project combines data driven approaches from AI on the one hand, and whole-brain computational modelling on the other. PhD opening: How do worms move in 3D? To address this question, we have built a 3D imaging system and have collected hours of footage. Prior work has focused on developing machine vision methods to reconstruct postures and trajectories; characterising postures and locomotion behaviours; and characterising and modelling locomotion strategies and foraging behaviours. This PhD can build on these foundations to perform exciting innovative experiments, and/or to build computational models of worm locomotion.

The Gatsby Computational Neuroscience Unit is a leading research centre focused on theoretical neuroscience and machine learning. We study (un)supervised and reinforcement learning in brains and machines; inference, coding and neural dynamics; Bayesian and kernel methods, and deep learning; with applications to the analysis of perceptual processing and cognition, neural data, signal and image processing, machine vision, network data and nonparametric hypothesis testing. The Unit provides a unique opportunity for a critical mass of theoreticians to interact closely with one another and with researchers at the Sainsbury Wellcome Centre for Neural Circuits and Behaviour (SWC), the Centre for Computational Statistics and Machine Learning (CSML) and related UCL departments such as Computer Science; Statistical Science; Artificial Intelligence; the ELLIS Unit at UCL; Neuroscience; and the nearby Alan Turing and Francis Crick Institutes. Our PhD programme provides a rigorous preparation for a research career. Students complete a 4-year PhD in either machine learning or theoretical/computational neuroscience, with minor emphasis in the complementary field. Courses in the first year provide a comprehensive introduction to both fields and systems neuroscience. Students are encouraged to work and interact closely with SWC/CSML researchers to take advantage of this uniquely multidisciplinary research environment.

The Gatsby Computational Neuroscience Unit is a leading research centre focused on theoretical neuroscience and machine learning. We study (un)supervised and reinforcement learning in brains and machines; inference, coding and neural dynamics; Bayesian and kernel methods, and deep learning; with applications to the analysis of perceptual processing and cognition, neural data, signal and image processing, machine vision, network data and nonparametric hypothesis testing. The Unit provides a unique opportunity for a critical mass of theoreticians to interact closely with one another and with researchers at the Sainsbury Wellcome Centre for Neural Circuits and Behaviour (SWC), the Centre for Computational Statistics and Machine Learning (CSML) and related UCL departments such as Computer Science; Statistical Science; Artificial Intelligence; the ELLIS Unit at UCL; Neuroscience; and the nearby Alan Turing and Francis Crick Institutes. Our PhD programme provides a rigorous preparation for a research career. Students complete a 4-year PhD in either machine learning or theoretical/computational neuroscience, with minor emphasis in the complementary field. Courses in the first year provide a comprehensive introduction to both fields and systems neuroscience. Students are encouraged to work and interact closely with SWC/CSML researchers to take advantage of this uniquely multidisciplinary research environment.

State-of-the-art machine vision models can predict human recognition memory for complex scenes with astonishing accuracy. In this talk I present work that investigated how memorable scenes are actually remembered and experienced by human observers. We found that memorable scenes were recognized largely based on recollection of specific episodic details but also based on familiarity for an entire scene. I thus highlight current limitations in machine vision models emulating human recognition memory, with promising opportunities for future research. Moreover, we were interested in what observers specifically remember about complex scenes. We thus considered the functional role of eye-movements as a window into the content of memories, particularly when observers recollected specific information about a scene. We found that when observers formed a memory representation that they later recollected (compared to scenes that only felt familiar), the overall extent of exploration was broader, with a specific subset of fixations clustered around later to-be-recollected scene content, irrespective of the memorability of a scene. I discuss the critical role that our viewing behavior plays in visual memory formation and retrieval and point to potential implications for machine vision models predicting the content of human memories.