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Field
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environment focused on large-scale AI. Experience with geometric deep learning, diffusion architectures, or related frameworks (e.g., OpenFold, AlphaFold2/3). Familiarity with Docker/Singularity
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fields. Individuals in this position will perform technical work supporting the collaboration's scientific research. Research Title: Research scientist: Optical trapping and sensing of nanoparticles (U.S
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Optoelectronic Devices & Single-Photon Diodes) to support an NRF-funded project on electrically driven single-photon-emitting diodes based on colloidal quantum wells. The role involves device fabrication, optical
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scattering (SAXS) tensor tomography Develop correlative multi-scale analysis pipelines integrating SAXS tensor tomography and high-resolution X-ray tomography, and complementary electron microscopy data (in
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industrial-level uniformity and spatial resolution by using the pilot 300-mm semiconductor process line available in collaboration with AIST. Metasurface-empowered optical cryptography scheme with
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of nonlinear optics through the integration of artificial intelligence. The successful candidate will lead projects that combine the development of Physics-Informed Neural Networks (PINNs) with advanced fiber
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. The successful candidate will apply a range of structural and biochemical approaches, including single-particle cryo-electron microscopy (cryoEM) and cryo-electron tomography (cryoET), to dissect the mechanisms
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, e.g. weekly meetings, journal clubs, seminars etc. There has been progress using GenAI, like diffusion models & flow maps, for inverse problems. The GenAI models act as data-derived priors, while
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culture. Key Responsibilities Design and implement optical interferometric imaging approach for functional neuroimaging Conduct experiments on cells and/or animals Analyze and summarize the experimental
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do things, especially considering recent advancements in AI technology. The position will include developing radiomics and deep learning models from contrast-enhanced computed tomography images