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for quantized and pruned neural networks, creation of quantized and pruned demonstration models, reproduction of state of the art, experiments in heterogeneous quantization Depending on expertise
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different methods of analysis used in the community, in particular linguistic probes (classifiers trained to predict certain linguistic properties from representations discovered by neural networks
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communications Quantum communications Computing & Networking: QuMIMO, Quantum Error Correction, Multi-partite systems, Q Network Coding, HQCNN - Hybrid Quantum-Classical Neural Networks Security & Logic: QRL
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Knowledge of deep learning architectures, graph neural networks, or uncertainty quantification Familiarity with HPC environments Language Requirements: Applicants must demonstrate at least B2-level
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Learning, particularly Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models, including hands-on implementation Strong understanding of machine learning models
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implementations (e.g., biophysical models), as well as models of machine intelligence (e.g., deep convolutional neural networks). We test the models' predictions in our empirical studies with human participants
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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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development, especially with neural networks. Experience with standard software development tools (Git, CI/CD, IDEs, issue tracking). Strong interest in academic research and willingness to pursue a PhD