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modern privacy-enhancing technologies (e.g. based upon synthetic data or using formal differential privacy guarantees) impact research integrity and reproducibility. This is an exciting line of research
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interdisciplinary research with leading academic groups across neuroscience, physics, and computer science, contributing directly to the future of neuromorphic systems. Key Responsibilities: Differentiate, grow and
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experiments. Excellent communication skills, including proven record of training researchers in tissue culture and stem cell techniques and neuronal differentiation Desirable criteria Knowledge
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"Mathematical Data Science" research group at the University of Vienna (led by Prof. Dr. Philipp Grohs) and the "Computational Partial Differential Equations" research group at TU Wien (led by Prof. Dr. Michael
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been shown to accelerate and improve the training procedure of SNNs by defining new cost functions that are differentiable and easier to optimize. They can also handle quantized weights, e.g., using
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differential chiral contrast (EDCC) imaging of emissive chiral molecules, yet again igniting and broadening the horizon of CPL research. In this project we set out to adapt and embed our patented CPL chiroptical
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/or their active counterparts. • To perform direct numerical simulations of the continuum partial differential equations of fluid dynamics, solid mechanics, soft matter or active matter