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Experience with VLSI design (Cadence tools, Verilog/VHDL, SPICE) Knowledge of neural networks and neuromorphic systems is a strong advantage Good programming skills (e.g., Python, MATLAB) and interest in
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. PV field system data analysis (time-series data analysis with JMP software and /or Python). Accelerated ageing procedures (IEC standards). PV module failure modes. Corrosion. Strong communications
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Python. Strong interest in combining computational and experimental chemistry. Excellent knowledge of written and spoken English. Excellent communication skills, especially for collaborating with others
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the programming language Python. Experience in computational chemistry. Basic knowledge about homogeneous catalysis. Strong interest in combining computational and experimental chemistry. Excellent knowledge
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packages and tools (e.g., Numpy, Pytorch, Tensorflow, ART). You have knowledge or familiarity with reverse engineering tools (e.g. NSA Ghidra, IDA Pro) You have experience with Python, C/C++, or low-level
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requirements for admission to the faculty's Doctoral Programme You must have good written and oral English language skills Experience with programming in Python or similar languages
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, preferably Reinforcement Learning (e.g., Q-learning, Deep Q-Networks) or other control algorithms. Proficiency in Python, MATLAB, or similar for data analysis, modeling, or AI implementation. Strong written
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) for general criteria for the position. Preferred selection criteria Background in programming (Matlab, python, …), familiar with Multi-body dynamic tools and good knowledge of statistics will be an advantage
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science and machine learning Knowledge with Python or Matlab. Application process Please send your CV, academic transcripts and brief rationale why you want to join this research project via the HDR
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-based computational homogenization. Experience using non-linear finite element software, e.g., Abaqus. Experience with programming using Python and Fortran. Experience with conducting experimental work