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materials and devices, calorics, heat transport, and thermal management. Experience with applied computational techniques and machine learning methods is a plus. Some postdoctoral research experience is
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-of-the-art in SLAM, situational awareness, computer vision, machine learning, robotics, and related fields Developing and implementing innovative solutions, validated through real datasets and experiments
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applicant: has a PhD degree in electrical, computer or biomedical engineering, computer science, data mining/machine learning, or a closely related area. has demonstrated the ability to perform independent
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automation software for high-throughput quantum-hardware preselection and characterisation using image recognition and machine learning tools. As R&D institution we are highly interested to disseminate
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physics, applied mathematics, machine learning, bioinformatics, biophysics, spectroscopy, image processing, ecological modeling, molecular biology, plant physiology, marine biology or an interest in gaining
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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facility, aligned with the evolving scientific and technological needs of the campus. Leverage cutting-edge computational tools, including machine learning and other emerging AI tools to accelerate
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ERC-funded postdoctoral fellow in theoretical developmental biology, using tools from applied mathematics, biophysics, and machine learning A talented and creative researcher is sought to take part
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through soft, disordered materials, including auto-regulated networks, composite soft solids, and exotic photonic biomaterials. The lab has two fully funded PhD and/or postdoctoral positions available
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approach to develop chemical probes, investigate biological mechanisms, and evaluate in vivo efficacy. In particular we use the promiscuous pregnane X receptor (PXR) and constitutive androstane receptor (CAR