PhD student in computational modeling of highdimensional visual category learning (m/f/x)

The lab seeks to understand the cortical basis and computational principles of perception and experience-dependent plasticity in the brain. To this end, we use a multimodal approach including fMRI-guided electrophysiological recordings in rodents and non-human primates, and fMRI and iEEG in humans. See https://doi.org/10.1016/j.cub.2023.01.011 and https://doi.org/10.1038/s41467-024-51543-y for recent examples of our work. The PhD student will play a key role in our research efforts in this area. The lab is located at Ruhr-University Bochum (https://www.rub.de) and the German Primate Center (http://www.dpz.eu). At both locations, the lab is embedded into interdisciplinary research centers with international faculty and students pursuing cutting-edge research in cognitive and computational neuroscience. The PhD student will have access to dedicated computing infrastructure, including high performance computing clusters. The project will be conducted in close collaboration with the labs of Fabian Sinz (https://sinzlab.org), Alexander Gail (https://www.dpz.eu/sensomotorik), and Igor Kagan (http://igorkagan.org). The main site for this part of the project will be Bochum. Ruhr-University Bochum provides a vibrant and stimulating neuroscience community with a strong community in computational as well as experimental neurosciences. The PhD student will have the opportunity to join the International Graduate School for Neuroscience (https://www.ruhr-uni-bochum.de/igsn/index.html) or the International Graduate School of Biosciences (https://www.biologie.ruhr-uni-bochum.de/biodek/promotion/index.html.en).

The Department of Cognitive Neurobiology of Caspar Schwiedrzik at Ruhr-University Bochum is looking for an outstanding PhD student with expertise in computational cognitive neuroscience and/or deep learning to join our team to the neural basis of mental flexibility. The project investigates neural mechanisms of high-dimensional visual category learning, utilizing computational approaches as well as neuroimaging and neurophysiology in humans and macaque monkeys, respectively. It is funded by an ERC Consolidator Grant (Acronym DimLearn; “Flexible Dimensionality of Representational Spaces in Category Learning”). The PhD student’s project will focus on developing deep learning “digital twin” models based on neural and/or behavioral data to investigate flexible multi-task learning.