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for reproducible, efficient and scalable training and inference on parallel, distributed and GPU-accelerated computing systems Benchmark the developed approaches against established methods, assessing
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; PyTorch; parallel and GPU computing Applicant Profile The internship is intended for a final-year Master’s student or engineering student specialising in applied mathematics, signal processing or
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and sufficient. Assigning meaning to prototypes is however a time-consuming task, as the various models tend to learn prototypes that do not carry sufficiently high-level meaning. A parallel line of
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, parallel research axes: pushing model accuracy on systems of several thousand atoms with an IWAE architecture; equipping it with the ability to quantify its own uncertainty — a prerequisite for any use
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Your Job In addition to completing your PhD degree, your primary objective is to establish a real-time processing architecture that improves gravitational wave detection sensitivity. By utilizing
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processing conditions Apply data-driven and machine-learning methods for multi-objective optimization of efficiency, reproducibility, and operational stability Study intrinsic material stability
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electrolyte layer. Planning, conducting, and evaluating proof-of-concept experiments for coating the metallic support structure Pretreatment and processing of oxide-ceramic powders Identifying the most
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analyses Electrode integration and processing studies: Incorporate catalysts into planar and 3D electrodes and investigate processing effects to ensure reproducible, high-performance assemblies
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and battery ageing models into an optimisation framework Processing and integrating real-world driving, charging, vehicle-availability, household-load and photovoltaic-generation profiles Developing
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measurements and quantifying their accuracy under different operating conditions and dynamic load profiles Processing and analysing experimental and time-series data and investigating the