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geoscience and remote sensing methods with AI and advanced computing technologies for Earth observation (EO) applications, including climate science, forestry, or agriculture. Your doctoral research
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quantum computing technologies have the potential to further accelerate selected computational tasks. The successful candidate will contribute to the development of quantum computing based solutions for
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design of hybrid electrical–optical computing architectures and (2) develop learning rules that are both technology-feasible and well-suited for machine-learning workloads. The project will consist among
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‑state physics and electronic transport phenomena A strong interest in charge transport physics, nanoscale devices, and thin film materials Hands‑on experimental mindset, enthusiasm for advanced
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concepts for alkaline battery operation Designing and fabricating porous iron electrodes Performing electrochemical evaluation in relevant cell configurations Applying advanced characterization methods
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neural network types for efficient hardware implementation Developing an FPGA-optimized design Performing device-aware training using both simulated and measured datasets Validation of the complete
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of computing parameters, increasing the memory resources required for their acceleration as well as exacerbating the criticality of data movement within the system on both energy efficiency and
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the induction. Our Benefits for You Cutting-edge research : You will work on challenges at the intersection of IC design and quantum computing, helping to shape the next generation of quantum
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and simulation of analog, digital, or mixed-signal circuits, including SPICE and related tools is strongly desired For algorithms projects, a strong background in coding and advanced mathematics is
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hyperpolarized spin states Constructing instrumentation to do low-field NMR experiments Your Profile Current master studies in physics, chemistry, materials science or similar Basic experience with NMR is