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telescopes. Explore Machine Learning techniques (CNNs, DBSCAN, Transformers, etc.). Implement low-latency computing accelerators on FPGAs. Contribute to open-source ML-on-FPGA tools (e.g., HLS4ML, FINN
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100%, Zurich, fixed-term The ODI group at the Institute of Machine Learning is looking for highly motivated postdoctoral researchers with expertise in reinforcement learning (RL) to join our team
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Supervision of BSc and MSc students Your profile We are looking for a highly creative self-motivated team player You have a PhD degree in Materials Science or Chemistry combined with Machine Learning In depth
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team members Perform accelerated optical degradation tests of transparent conductive materials Apply machine learning techniques for data analysis and time-series forecasting Collaborate in a
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are also expected. Profile PhD in Data Science, Computer Science, Mechatronics, Remote Sensing, Engineering Geology or other related discipline Demonstrated expertise in machine learning and computer vision
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for the position are expected to have a PhD in Chemistry, Material Science or Chemical Engineering. Experience in the fields of material synthesis as well as the physical and electrochemical characterization
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progress Profile Required PhD in Computer Science with the focus on AI Proficiency in python programming Strong expertise in machine learning and deep learning frameworks (especially PyTorch) Demonstrated
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Geology or other related discipline Demonstrated expertise in machine learning and computer vision algorithms is necessary, with an emphasis on object tracking, optical flow and sensor fusion Knowledge
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of circularly polarized / chiral phonons in quantum paraelectric materials. Job description The postdoctoral researcher will develop machine-learned force fields trained on density functional theory (DFT) outputs
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for the position are expected to have a PhD in Chemistry, Material Science or Chemical Engineering. Experience in the fields of material synthesis as well as the physical and electrochemical characterization