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Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig | Leipzig, Sachsen | Germany | 2 months ago
microscopy and ultra-high-field imaging Neuroimaging software (SPM, FreeSurfer, hMRI toolbox) Programming in Python and/or MATLAB Multimodal data integration Complementary elemental analysis (e.g., PIXE, XRF
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, differential equations, linear algebra to answer how neural computations are implemented in neural networks, both in individual neurons and at the level of large circuits. We are computational group that works
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/ ). Together, CAHBIR, the CCNP, and a broad network of Rutgers-affiliated clinical partners provide an exceptional environment for training in clinically focused computational and cognitive neuroscience research
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Contributions Contribute to the continued development and maintenance of UXarray, including analysis operators, grid subsetting, remapping, and plotting capabilities for unstructured mesh data. Help develop and
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studies in participatory sustainability governance from all world regions. Data analysis support (WP 3): You will support and advise the four PhD researchers in applying quantitative and qualitative methods
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environmental data within a global collaborative network. Postdoctoral Positions We seek candidates with expertise in: Neuroimaging / biomedical engineering Computational neuroscience / bioinformatics Multi-omics
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measurements, advanced data analysis, and numerical simulations. You will investigate magnetic states and excitations in novel materials using neutron scattering, muon spectroscopy, and complementary laboratory
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both method development and empirical research, the latter in collaboration with scientists from other departments. We offer a research environment with access to national and international networks. Our
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learning for the analysis of population-scale networks. The position focuses on two pillars of trustworthiness: explainability and privacy-preserving learning. This research direction connects closely
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. The project focuses on the intersection of deep reinforcement learning, probabilistic modeling, and bio-inspired architectures (such as Spiking Neural Networks) to achieve sample- and energy-efficient robust