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Field
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learning, with a particular focus on differential equation-driven frameworks. The research will be fundamentally oriented, and the overall mission is to develop computationally efficient and statistically
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” because early abnormalities are missed by current diagnostic methods. This project sits at the cutting edge of biomedical engineering, AI, and respiratory medicine, developing a non-invasive, low-cost
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uncertainties, with a strong focus on safety-critical bilateral teleoperation (e.g., tele-surgery). The doctoral research will focus on the development of fault-tolerant and adaptive control architectures
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application! Your work assignments We are looking for a PhD student to work on the development of novel spatio-temporal machine learning methods. Our world is inherently spatio-temporal, i.e. physical processes
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project aims to develop advanced control and planning algorithms that enhance robustness and safety, ensuring reliable performance even in the presence of magnetic fields and other uncertain conditions. The
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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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requirements: Experience using deep-learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating on scientific projects. Publications on deep-learning topics. 4. Work Plan
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be duly proven at the time of hiring. 2; 3. Preferred requirements: Experience using Machine Learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating
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critical functions in inter-cellular communication, controlling tissue development, homeostasis and repair, inflammatory and immune responses, neuronal connectivity, and symbiosis with bacteria. However
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for image classification and other domains against existing open source LLM (e.g., Llama 3, Phi-3), as well as develop new kinds of attacks, for example based on evolutionary algorithms. 2. Investigate