2,861 data-science-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" PhD positions
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research. You will strengthen the data science and machine learning activities of IAS-9 by developing core AI methods with applications to electron microscopy and materials discovery. You will work in a
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individual visionary figures or to synergetic team efforts.
Through a systematic analysis of at least 50 key examples from the history of science – ranging from antibiotics to DNA structure
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two projects share the goal of developing stable and reliable perovskite-based tandem photovoltaics, while maintaining complementary scientific responsibilities. FZJ focus: automated and data-driven
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Nature: Use sound/network data as insight for green transitions Your qualifications The candidate must have: Graduated at Master’s level in ecology, biology, applied mathematics, computational science
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composites, ceramics, and metals (Composites: https://doi.org/10.1016/j.matt.2026.102809 , https://doi.org/10.1021/acsmaterialslett.5c00407 ;Ceramics/Metals: https://doi.org/10.1002/adma.202504951
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without Centralized Training Data”, https://ai.googleblog.com/2017/04/federated-learning-collaborative.html [2] “Learning with Privacy at Scale”, https://machinelearning.apple.com/research/learning
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into the learning mechanism. The integrating should enable to guarantee certain properties of the learned functions, while keep leveraging the strength of the data-driven modelling. Most of, if not all
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This PhD project will focus on developing, evaluating, and demonstrating advanced data analytics solutions to a big data problem from aerospace or manufacturing system to uncover hidden patens
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BI invites applications for doctoral scholarships in Data and Decision Sciences, affiliated with the Department of Data Science and Analytics. BI Norwegian Business School is one of Europe's largest
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candidate must also possess strong communication and organizational skills. Qualifications a..Qualifications A PhD in Computer Science or Electrical Engineering with biomedical data analysis emphasis