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with strong analytical and numerical skills, and backgrounds in physics, theoretical neuroscience, applied mathematics, computer science, engineering, or related fields. Experience in relevant research
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; Chemistry; Computer Science; Ecology; Engineering; Environmental Sciences; Mathematics/Mathematical Sciences; Medicine; Natural Sciences; Physics; Psychology; Veterinary Sciences. This Fellowship is intended
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postgraduate degree, ideally a PhD, in statistics, machine learning, or a related field. Experience of developing new statistical methods and a strong working knowledge of a statistical software package, such as
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, Physics, Computer Science, or a related field. Hands-on experience with computational materials methods (e.g., DFT, molecular dynamics, machine learning force field simulations). Proficiency in Python
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performance computing systems in a way that is suitable for operational use. Implement and test different ML architectures, such as convolutional and encoders-decoder ANNs for predicting flooding in Bihar and
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training programme or within 4 years full-time equivalent of completing their training programme are eligible to apply. Clinical work during the PhD will be limited to one day per week. Doctors and dentists
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to invite applications for the KICP Postdoctoral Research Fellowship program. Scientists receiving a PhD in physics, astrophysics, astronomy, or related fields between June 2022 and September 2026 are welcome
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dedicated to technology such as state-of-the-art light microscopy, nano- and micro- fabrication, and computing We are seeking a Research Fellow with expertise in transmission electron microscopy (TEM) to join
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, cultural practices and technologies between China and the Mediterranean or any sub-region between the two at any period up to the present day. This Research Fellowship is part of a broader programme, which
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The School of Materials Science and Engineering (MSE) provides a vibrant and nurturing environment for staff and students to carry out inter-disciplinary research in key areas such as Computational