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optimisation Experience in one or more of the following areas would be advantageous: EO or geospatial data analysis, parallel or distributed computing, GPU programming, Linux and containerised
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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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-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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computing Excellent programming skills (e.g., Python, C/C++, Julia, or MATLAB) Interest in quantum computing, quantum algorithms, and hybrid quantum–classical computing. Previous experience is considered
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neuroscience or a related field of study Extensive experience with programming (Python or Matlab) Passionate about human neuroanatomy and cognitive neuroscience Highly advanced and flexible command of
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statistical physics; expertise in quantum information is particularly appreciated Strong programming experience and/or strong background in analytical methods Very good command of written and spoken
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Current master's student in Process Systems Engineering, Computational Engineering Science, Chemical Engineering or a comparable program Programming experience in Python; experience with PyTorch is a