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Postdoctoral Researcher in Machine Learning of Isomerization in Porous Molecular Framework Materials
broad range of applications. Computational chemistry and Machine Learning increasingly underlies MFM research to search or screen candidate MFMs prior to synthesis. A major drawback when applying
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-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human
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-quality robotics research in the areas of robot grasping and manipulation, kinematics and mechanisms, sensing, and human-robot interaction. Within CORE, SAIR focuses on multimodal machine learning for human
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superconducting qubits and millikelvin electronics Did you recently get your PhD in circuit quantum electrodynamics (cQED) and are now looking into taking the full potential of your skills into use for making new
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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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this suits a candidate with a background in optical systems / imaging, or with more experience in machine vision, or systems control and automation, or data interpretation. A candidate would not be expected
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. Ready to be part of our team? Let’s shape the future together! About the team: The Computational Materials Discovery group is looking for a postdoctoral researcher working in the field of machine learning
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students and PhD students. Applicants will have, or be close to completing a PhD in a relevant field and possess relevant experience, in the area of probability or statistical machine learning. They will
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the development of hierarchical computational materials discovery schemes combining random structure searching, machine learning, atomistic, and density functional theory (DFT) calculations to accurately and
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bioinformatic workflows. Familiarity with biomedical ontologies and text mining on Electronic Health Records and biomedical literature Knowledge of machine learning / deep learning with an interest in