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and analysis methods for realistic CO2 electrocatalysis, with a focus on parallel investigations and accelerated aging. Dissect degradation processes of CO2 electroreduction catalyst, electrodes, and
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analysis methods for realistic CO2 electrocatalysis, with a focus on parallel investigations and accelerated aging. Dissect degradation processes of CO2 electroreduction catalyst, electrodes, and ionomer
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summary of the latter see: p3.snf database In parallel we investigate the role of PI3K in allergy, metabolic control, obesity, diabetes and cancer. To design, execute and analyze biochemical, biophysical
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-solving, and collaborative mindset Highly desirable Experience with distributed training or ML systems Knowledge of privacy-enhancing technologies and parallel programming Experience with multilingual AI
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opportunities but requires robust biological protocols and stable bioelectronic interfaces. In parallel, biomolecules and their complexes can be probed and controlled using CMOS micro-/nano-electronics, enabling
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Network with 15 funded 3-year PhD positions in parallel. Your profile Master Degree in environmental/natural sciences or engineering, or similar. Experience with developing computational models Preferably
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parallel and distributed systems, including performance tuning Programming and tooling such as C/C++, Python, CUDA, OpenMP, and Spack Linux-based systems, scripting, Slurm, and general systems engineering
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properties. In parallel, they continuously sense and respond to diverse mechanical cues from their environment, including adhesion, stiffness, tension, shear, pressure, and confinement. These cues
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biological scales. Project background Cells are mechanically heterogeneous systems composed of proteins, membranes, and compartments with distinct physical properties. In parallel, they continuously sense and
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of computer graphics fundamentals, numerical methods, and GPU/parallel computing concepts. Experience with at least one major deep learning framework (PyTorch preferred). Excellent problem-solving skills and