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programming machine learning models (e. g. PyTorch/Jax) and familiarity with spiking-network frameworks (e. g. snnTorch or Norse) or neuromorphic hardware is an advantage. An active interest in
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closely related field, completed by the start of the position Have practical experience in machine learning with Python, including training neural networks in PyTorch or a similar framework Have a solid
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introduce state-of-the-art AI tools, accelerating technology transfer and hands-on learning opportunities. Design and deploy high-performance AI systems using GPUs and hardware accelerators. Job
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extent of disruption required to stop unsafe behaviour, and the reliability of proposed protective measures. The emphasis can be adapted to the student's background, including machine learning
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programming and applied mathematics, and interested in neural networks and their hardware implementation. Some experience in machine learning would be an asset. Prior knowledge of emerging memory
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. Previous experience in magnetism, spin dynamics, nanofabrication, electrical measurements, 2D materials, programming and machine learning would be advantageous. Candidates should also be able to work
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<span lang="en">TUD Dresden University of Technology</span> | Dresden, Sachsen | Germany | 3 minutes ago
for use-case demonstrators
Requirements:
- university degree (Master’s or equivalent) in computer science, electrical engineering, machine learning or
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members to apply. You will: Design and implement machine learning systems, models, and schemes Research and apply state-of-the-art machine learning prototypes to sponsor specific domains Search and
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implement machine learning systems, models, and schemes Research and apply state-of-the-art machine learning prototypes to sponsor specific domains Search and select appropriate data sets before
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. Applied 18, 064028 (2022). Keywords machine learning; reinforcement learning; spin readout; optimal control; circuit design Eligibility citizenship Open to U.S. citizens level Open to Postdoctoral