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research using Python, with PLECS available as the simulation software. You will document and present your work and results. What you bring to the table You are studying electrical engineering or a similar
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the integration of both Convolutional Neural Networks (CNNs) and Spiking Neural Networks (SNNs). This position will involve training and optimizing these neural networks using Python frameworks, including CUDA
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that you will implement in Python, before evaluating their added value and usability on our test benches. In addition, you will also model and simulate offshore wind turbines, be involved in the further
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? You have good knowledge of Python? You are interested in wind turbine simulation tools, like FAST, Hawc2, Bladed or QBlade? Great, we are looking forward to your application. What you can expect What we
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such as R, Python, or Java Unix / HPC experience very good written and spoken English pro-active learning ability to work independently as well as a team member excellent communication, organizational, and
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experimental and laboratory work Experience with programming in Python Fluency in speaking and writing English Previous experience with scanning probe microscopy would be an advantage Our Offer: We work on the
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Familiarity with structured reasoning, chain-of-thought processes, and agent-based systems is beneficial Strong programming skills (preferably Python); experience with high-performance computing (HPC
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Basic knowledge of programming (e.g. Python, C, Matlab), or willingness to learn Excellent cooperation and communication skills and ability to work as part of the team Excellent organizational skills High
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or Mechanical Engineering – strong background in machine learning, deep learning and / or computer vision – good programming skills in Python (and C++) – basic knowledge of optics including concepts like PSF, MTF
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Biology, Physiology, or a closely related discipline Demonstrated expertise in molecular biology techniques (including metagenomics), bioinformatics and coding skills (R, Python, or equivalent) Proven