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Field
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with training neural networks to develop the next generation of optical microscopes. You will have the opportunity gain skills in optical instrumentation and imaging, AI and machine learning, and in
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, or computational modelling, and you should be comfortable working with large datasets and modern deep learning frameworks. Experience with neural network design, optimisation techniques, or scalable computing is an
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to work in. Research groups: Computational Neurosciences Computer Graphics and Ecological Informatics Computer Networks Computer Security Databases and Information Systems Data Fusion Data Science
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1 will focus on developing new graph-theoretic frameworks for analyzing graph learning models, such as Graph Neural Networks or Graph Transformers. PhD position 2 will focus on designing scalable
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science, physics, or related fields Coursework in algorithms, computational complexity theory, and information theory Relevant coursework and experience in spiking neural networks, and statistics A strong
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programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. In particular, you will be part of the Causality team under the supervision
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on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms that protect the integrity and reliability of deployed AI
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, to how epigenetics regulates cellular identity or neural memory. Activities and responsibilities The research group of Dorothee Dormann offers the following PhD project: In the Dormann lab, we study how
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in C++ and/or Python is expected, and experience in model analysis and parameter optimisation is beneficial. Experience in machine learning and neural networks is desirable. The successful applicant
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lightweight AI models suitable for real-time execution on constrained platforms using techniques such as model compression, quantization, and hardware-aware neural network design. Investigating mechanisms