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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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experience in common deep learning frameworks (e.g., PyTorch and TensorFlow) would be a benefit; The qualities to carry out independent research, demonstrated e.g., by the grades obtained in your (under
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the Future 5G/6G Deployments with Millimeter Wave Integrated Circuit Interfaces Generated by Deep Computer Vision. This project is funded by FCT/MECI through national funds and when applicable co-funded EU
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learns auditory information. Methodologically, the project will explore the integration of information-theoretic decompositions, deep neural architectures, and large language models (LLMs) as powerful
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learning, non-Hermitian systems The Quantum AI lab at ETH (Prof. Juan Carrasquilla ) invites applications for PhD positions to work at the intersection of computational quantum many-body physics, machine
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experience with deep learning, machine learning and/or time series analysis. Good programming skills in Python or similar languages. Experience with using machine learning in the context of neuroscience
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. The period of employment is governed by the Fixed Term Research Contracts Act (Wissenschaftszeitvertragsgesetz – WissZeitVG). Supervisory team: Supervisor: Prof. Dr. Martin Tajmar, Co-Supervisor: Dr. Pekka
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technology and/or computer graphics as well as interest in fundamental research and experimental working. Strong skills in VR, psychophysics, deep learning or computer simulation are another advantage Job
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investigate deep learning methods for local data augmentation and adaptive point density control, addressing the anisotropy and uneven sampling typical of urban LiDAR. You will work on a four-year doctoral