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learning pipeline in Python (using e.g. PyTorch) - validation of your results in collaboration with colleagues from various application areas (cross-disciplinary) - publication and presentation of your
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pipeline in Python (using e.g. PyTorch) validation of your results in collaboration with colleagues from various application areas (cross-disciplinary) publication and presentation of your scientific results
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use other programmes, such as Python, Stata, or SAS. Application deadline Please check the HDS PhD website in autumn 2025 for updated information on 2026 HDS PhD programme admissions. The application
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problems Proficiency in data analysis and programming using at least one statistical program such as R, Python, or similar programming languages Experience with GAMS, GTAP, and Exiobase is an asset. Skills
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chemistry, or Applied Mathematics or Information Technology. Experience with numerical programming, machine learning or data analysis, preferably with Python. Solid background in technical mathematics
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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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(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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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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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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, 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