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for back-end-of-line (BEOL) integration with CMOS. Key responsibilities include: • Develop and optimize ALD processes for oxide semiconductor channel materials (e.g., IGZO, ITO, IZO) and BEOL-compatible
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, programmability, high linearity and low-noise signal conditioning including optimal dynamic range and bandwidth utilization. This position offers a unique opportunity to drive research at this critical intersection
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Researcher (R1) Positions PhD Positions Application Deadline 6 Dec 2026 - 23:59 (Europe/Warsaw) Country Poland Type of Contract Other Type of Contract Extra Information Scholarship under Doctoral School Job
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the metroplex. Minimum Education and Experience Ph.D in a related field. Preferred Education and Experience A PhD in engineering with an emphasis in real-time optimization (e.g., extremum seeking) for dynamic
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, shape, and optimize granular composite (catalytic) materials that combine electrical conductivity, mechanical robustness, and fluidizability for EBFBR. Composite formulation and processing routes (e.g
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families we serve, including those from across the globe. The Senior Medical Laboratory Scientist supports patient care through the development, evaluation, validation, implementation, and optimization
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purchased or synthesized and subjected to biological evaluation. Promising results may lead to medicinal chemistry studies aimed at characterizing structure–activity relationships and optimizing selected
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control and optimization strategies, run high-performance numerical experiments, analyze data, and communicate your findings through journal publications and conference presentations. The ideal candidate
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 2 months ago
application notes for the MIT.nano user community. REQUIRED: A PhD in an engineering or science discipline related to micro/nano-fabrication; a minimum of two years of related experience in a research
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optimally combined to deliver models with extremely constrained compute and memory footprints without compromising performance. This includes training spiking neural networks with multiple plasticities