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, electrical interfaces, mechanical module design or control. The research will contribute methods for understanding resilient machines as physical systems and for maintaining human control as their ability to
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design of hybrid electrical–optical computing architectures and (2) develop learning rules that are both technology-feasible and well-suited for machine-learning workloads. The project will consist among
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background in machine-learning and FPGA programming and be familiar with mixed-signal integrated circuit design.
What we offer:
- The possibility to
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with transistor-level circuit design and simulation What We Offer We provide GPU computing for machine learning on Aalto's Triton high-performance computing cluster, alongside industry-standard
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-level circuit design and simulation What We Offer We provide GPU computing for machine learning on Aalto's Triton high-performance computing cluster, alongside industry-standard circuit simulation tools
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uncertainty, since misclassifications can lead to poor energy management decisions or inappropriate building automation responses. The goal of this project is to design and validate a machine learning
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applications and algorithms that can leverage associative memories, including machine learning and LLMs, genomics, and classical data science methods (tree models, decision trees, finite automata). We
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of Electrical Engineering. Thus, please see the student information and admission criteria at https://www.aalto.fi/en/study-options/aalto-doctoral-programme-in-electrical-engineering . Desired
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in this role, you should have: A master’s degree, or be close to completing a master’s degree, in speech or audio processing, machine learning, signal processing, computer science, electrical
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Future robots must perceive, predict, communicate, and decide reliably in complex, human–robot shared environments. The Mobile Robotics ( https://www.aalto.fi/en/department-of-electrical-engineering