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environnementales auxquelles les ouvrages sont soumis et les dégradations observées lors des inspections. Pour cela, le projet s'appuie sur des Physics-Informed Neural Networks (PINNs) capables de
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embedded AI. To overcome this bottleneck, neuromorphic electronics based on spiking neural networks (SNNs) and the compute-in-memory (CIM) paradigm is emerging as the most promising path. While the research
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design and training of neural networks Practical experience in FPGA development or embedded hardware design, including HDL (VHDL/Verilog) or high-level synthesis (HLS) tools Proficiency in programming
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Garching . Here, you will develop general-purpose, scalable linear solvers for training neural networks that represent solutions to PDEs . Your work will involve utilizing GPU code generators for matrix
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. About the Role The successful candidate will be expected to: • Carry out high-quality research in quantum neural networks, quantum information, quantum optics. • Participate in the development of
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in developing gene perturbation models that combine foundation models (FMs) and graph neural networks (GNNs) to accelerate therapeutic target identification. GenePPS aims to overcome current
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models. o Python, R or another programming language; previous experience is valuable but not required. o Computational/mathematical models (artificial neural networks, dynamical systems), behavioral
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. A novel drive-by bridge damage classification method will be developed using the concept of physics-guided deep neural networks. For this purpose, a novel cross physics-data domain loss function will
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from a Single Image https://data.vision.ee.ethz.ch/cvl/publications/papers/proceedings/eth_biwi_01229.pdf [2] Cao et al. (2019). Rank consistent ordinal regression for neural networks with application
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November 2026. Funding source : Q-ONE project, "Quantum Optical Networks based on exciton-polaritons", funded by the European Commission (Grant Agreement No. 101115575). Project website: https