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personal qualifications A PhD in data science, statistical genetics, quantitative genetics, bioinformatics, statistics, computer science, or closely related fields (required). Experience with large-scale
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. The ICN2 PhD Fellowships Program is possible thanks to the Severo Ochoa Programme by the Spanish Ministry of Economy and Competitiveness to support Research Centres of Excellence and the International PhD
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journals such as S&P, USENIX, SIGMOD, VLDB, NeurIPS, ICML, AAAI, or ICLR. Job Requirements: A PhD degree in computer science, computer engineering, mathematics, statistics, or a related discipline with a
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candidate in the area of machine learning for IoT networks. The candidate must hold (or about to complete) a PhD in the related fields shown below. The candidate is expected to have hands-on experience in
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: Machine learning/deep learning model development for biomolecular data analyses and prediction Research Area: Data science and computational chemistry Required Skills: A Ph.D. in relevant field within
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Details Title Postdoctoral Fellow in Deep Learning Theory and/or Theoretical Neuroscience School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Position
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machine learning conferences/journals Job Requirements: PhD in Mathematics and/or Electronics and/or Computer Science Ability to work independently and as part of a team with strong initiatives Good
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in shaping the future? PhD position There is a vacancy for a PhD Research Fellow in comparative mitochondrial dynamics, combining experimental and computational work, at the Department of Mathematics
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Scholarship Smithsonian Institution: Fellowship Program Application is required. Key scholarship details Application status Open for applications Benefit amount Up to $150,000 Eligible study level
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Research Fellow in AI for Healthcare (1 FTE) – ArcHub ICUSafeNotes-School of Computer Science 011287
the Machine Learning Research Group in the School of Computer Science, working under the direction of Professor Michael Madden. The group focuses on new theoretical advances in machine learning, motivated by