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(beyond model training) Solid programming skills (Python required; C++/CUDA a plus depending on simulations) Interest in physics-based simulation, numerical methods, or computational engineering Motivation
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of scientific programming (e.g., Python or R) Experience in handling large datasets is an advantage. Interest in natural language processing, text mining, and machine learning. Interest in the societal
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is desirable. Basic understanding of embedded systems and processor architectures. Strong programming skills (C/C++, Python; hardware description languages such as HLS or VHDL are an advantage
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of LLMs is desirable. Basic understanding of embedded systems. Familiarity with fault detection, system reliability, or troubleshooting techniques. Strong programming skills (C/C++, Python; hardware
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, advanced programming in Python) in small classes of max. 10 participants. Lecture series: QMB students suggest, invite, and host external speakers at this event. The lectures on QMB-relevant topics
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the analysis and simulation of analog, digital, or mixed-signal circuits, including SPICE and related tools (LTspice, Cadence, MATLAB, Python) Excellent communication skills and ability to work in a team are
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engineering Very strong mathematical and algorithmic background Programming experience (Python, C++, etc.) Familiarity with parallel programming frameworks (e.g. MPI, CUDA) Fluent in written and spoken English
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with emerging memory devices Experience with simulation tools (LTspice, Cadence, MATLAB, Python) Interest in brain-inspired computation, energy-efficient hardware, and experimental validation Ability
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, Computational Biophysics, or a closely related field Strong programming skills (e.g., Python, C/C++) Knowledge of machine learning frameworks (e.g., PyTorch, TensorFlow) Very good English language skills, ability
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(command line-based) and scripting languages such as R, Python, Unix/shell Excellent command of written and spoken English Ability to work both independently and in a collaborative, interdisciplinary