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, institutional visits, and other events (in-person and virtual) Qualifications: Academic degree in the physical sciences, PhD preferred but not necessary Previous experience in academic publishing Fluency in
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Communications AI & Computing (https://www.nature.com/commsaicomp/ ). Communications AI & Computing is a multidisciplinary open-access journal publishing high-quality research across computational sciences and
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cutting-edge research in areas such as pattern recognition, automation science, complex systems, AI for Science, robotics, machine learning, computer vision, natural language processing, biometrics, medical
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28 Mar 2026 Job Information Organisation/Company The University of Western Australia - India Research Field Computer science Researcher Profile Recognised Researcher (R2) Leading Researcher (R4
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. Experience, Skills & Qualifications: Essential A PhD in computational biology (or closely related) areas. A keen interest in research both within and beyond your specialty. The ability to absorb and understand
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about the lab at: https://mbzuai.ac.ae/study/faculty/natasa-przulj/ and https://przulj-lab.github.io/ Qualifications PhD in Computer Science, Mathematics, Physics, Bioinformatics, or a related
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Title: Associate or Senior Editor, Nature Reviews Computing Location: Shanghai, Madrid or Pune - hybrid working model Closing date: April 22nd, 2026 About Springer Nature Springer Nature is one
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Faculty Positions in School of Al for Science / School of Electronic and Computer Engineering, PKUSZ
for Materials Science, AI for Life Science, and Computational Mathematics. We welcome candidates whose research aligns with these directions and who are eager to contribute to an open, cutting-edge, and cross
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to sustainability research to join Communications Sustainability (https://www.nature.com/commssustain/ ). Communications Sustainability is a multidisciplinary open-access journal publishing high-quality research
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applications for faculty positions in Computer Science. Faculty specialising in data science, machine learning (deep learning, reinforcement learning, multimodal learning), Generative AI, and computer graphics