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./week) TV-L E 13for the DFG Project " The neural dynamics of causal evidence accumulation in multisenso-ry perception" as of 1.1.2026 or as soon as possible, limited for 3 years. The project investigates
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. Strong coding skills for programming neural networks, machine learning and machine learning software frameworks (e.g. PyTorch or Jax) is a must. The ability for creative and analytical thinking across
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, C++, ROS, ML/AI frameworks such as PyTorch/TensorFlow). Background in at least one of: intelligent robotics, neural networks, cyber-physical systems, or explainable AI. Excellent English communication
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techniques and neural network techniques to adjust high-resolution X-ray spectra and infer physical properties of the emitting plasma. · Developing algorithms that optimise the adjustment of high-resolution X
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addresses this challenge in two ways: We investigate the fundamental neural mechanisms that control movement. We explore engineering-based solutions to restore function when these pathways are disrupted. As a
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models such as Random Forest and Neural Networks to help understand and predict pairwise interactions between pollinators and plant species. - Software Engineering: integrate models into a standalone
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shifts in cell state and cell fate. Integrate spatial transcriptomics data to anchor these predictions in tissue context. Develop machine learning methods (e.g. graph neural networks, variational
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organisms age or how our DNA is repaired, to how epigenetics regulates cellular identity or neural memory. Activities and responsibilities The research group of Katja Luck offers the following PhD project
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this, we focus on self-supervised denoising, where models learn to restore images using only the noisy data itself — without requiring clean references. Existing approaches often rely on convolutional neural
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-type hydrogels Linkopings Universitet (LiU): development of the neurohybrid network The work will be done in close collaboration with the other members of the FADOS network. Further information