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-loop workflow from material and process discovery to subcell validation and transfer to tandem devices. Your main tasks will include: Establish a stable and reproducible p–i–n wide-bandgap perovskite
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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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gaussian representation meets perception models for bev segmentation. In : 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. p. 2250-2259. [3] KERBL, Bernhard, KOPANAS
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. B 106, L161110 (2022). [2] A. Castellano, R. Béjaud, P. Richard, O. Nadeau, C. Duval, G. Geneste, G. Antonius, J. Bouchet, A. Levitt, G. Stoltz, et F. Bottin, Machine learning assisted canonical
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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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and material savings). One important first step is to achieve ohmic contacts on n- and p-type regions, which can be achieved through electrostatic doping developed with our partner, Stanford University
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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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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