You cannot apply for this job anymore (deadline was 15 Dec 2019).
Browse the current job offers or choose an item in the top navigation above.
As a PhD candidate, you will work on interdisciplinary projects combining computational neuroscience and machine learning in order to gain insight into the computational principles of information processing in the primate visual system. The main element of the work will include the development of and experiments with deep (recurrent) neural network models, simulated agents, and theoretical work aimed at better understanding which representational features emerge in the model system. The computational work is compared against large-scale data from neuroscience experiments, including functional Magnetic Resonance Imaging (fMRI), as well as high-density electrophysiological recordings and behavioural experiments. Furthermore your work includes dissemination of the scientific results in high-impact outlets, assistance with the preparation of research grants, and the release of open datasets and analysis tools.
Fixed-term contract: You will be appointed for an initial period of 18 months, after which your performance will be evaluated. If the evaluation is positive, the contract will be extended by 2.5 years.
We want to get the best out of science, others and ourselves. Why? Because this is what the world around us desperately needs. Leading research and education make an indispensable contribution to a healthy, free world with equal opportunities for all. This is what unites the more than 22,000 students and 5,000 employees at Radboud University. And this requires even more talent and collaboration. You have a part to play!
Our research group aims to understand the computational processes by which artificial and natural agents can efficiently and robustly derive meaning from the world around us. We ask how the brain acquires versatile representations from the statistical regularities in the input, how sensory information is transformed in the cortical network, and which information is extracted by the brain to support higher-level cognition. To find answers to these questions, we develop and employ machine learning techniques to discover and model structure in high-dimensional neural data. As a target modality, we focus on vision, the most dominant of our senses, both neurally and perceptually. To gain insight into the intricate system that enables us to see, the group advances along two interconnected lines of research: machine learning for discovery in neuroimaging data, and deep neural network modelling of natural intelligence in artificial systems. This interdisciplinary work combines machine learning, computational neuroscience, computer vision, and semantics. Our work is therefore at the heart of the emerging field of cognitive computational neuroscience.
The group is based at the Donders Institute for Brain, Cognition and Behaviour. The Donders Institute is a world-class interfaculty research centre that houses more than 600 researchers devoted to understanding the mechanistic underpinnings of the human mind. Excellent, state-of-the-art research facilities are available for the broad range of neuroscience research that is being conducted at the Donders Institute. The Institute fosters a collaborative, multidisciplinary, supportive research environment with a diverse international staff. It was recently recognised as a 'very stimulating environment for top researchers, as well as for young talent'. English is the lingua franca at the Institute.
We like to make it easy for you, sign in for these and other useful features: