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management systems (e.g., Snakemake). Experience or knowledge of artificial intelligence, such as implementing neural network models, is advantageous, and ideally, ML/AI frameworks such as PyTorch. Strong
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Associate Professor Harri Lähdesmäki. The position offers a broad local research network in Bayesian machine learning and computational biology. Your network and team Dr Martinelli is an independent
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to start imminently, and before mid October this year. The project intended for the post-doc is part of a collaboration within my group on the Google Willow Chip project (https://www.bbc.co.uk/news
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Yale University School of Medicine - Department of Neuroscience | New Haven, Connecticut | United States | 26 days ago
are changing all aspects of neuroscience from analysis of neural activity to network modeling to imaging to bioinformatics to molecular modeling. We are enthusiastic about candidates using computational
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NTNU Norwegian University of Science and Technology | Trondheim, Sor-Trondelag | Norway | 25 days ago
Implicit Neural Representation (INR), a class of neural networks, for reconstructing MR images directly from MR signals. By incorporating a physical model of the MR signal into the training of the INR
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the differential geometric structure of deformable objects into novel continuum physics-inspired neural representations, serving as the bedrock of a physically-grounded manipulation pipeline
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structure, state, control commands, external disturbances, and environmental conditions to predict interaction effects. Unlike robot-specific neural network models, the proposed approach aims to learn a
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Eindhoven University of Technology (TU/e) | Eindhoven, Provincie Noord-Brabant | Netherlands | about 2 months ago
inspired by biological neural systems. In particular, neuromorphic photonics aims to develop artificial photonic neurons that process information through sparse, event-driven optical spikes, enabling
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search committee chair by email at [email protected]. Review of Applications Begins On 11/13/2025 Website https://engineering.msu.edu/about/departments/bme Department Statement The Department of
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-driven modeling and simulation, pattern recognition, brain-inspired or spiking neural network approaches, predicting material properties, optimizing processing parameters for next-generation energy