2,939 phd-studenship-in-computer-vision-and-machine-learning Fellowship research jobs
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data; physics-based ML for turbine design and
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. The position requires participation in the Faculty of Social Sciences' organised research education programme (PhD programme) and the completion of a doctorate in sociology or human geography. The candidate who
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to complete the final exam. Desired: Familiarity with statistical and machine learning techniques. Knowledge about molecular biology and/or gene regulation. Experience with nanopore sequencing, Hi-C, ribosome
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% teaching component. The Department teaches in all the sub-fields mentioned above. The successful candidate will be part of the Faculty’s PhD programme. The work is expected to lead to a PhD in political
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the University’s international reputation as a hub for cutting-edge research. Candidates must have a PhD degree in Power Electronics, Machines, and Drives or a closely related field, with a proven track record in
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team to drive innovation in plant breeding. Key responsibilities will include: Research: Conduct and publish high-quality research, develop a coherent research program, and actively pursue competitive
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Econometrics Virtual power plants Power systems and/or power electronics Machine learning Renewable energy systems Advanced statistics Language requirement: Good oral and written communication skills in English
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Internal Number: 6415021 Postdoctoral Fellow, Bioinformatics, Brain Tumor Drug Discovery and Development Program University of California Los Angeles Requisition Number: JPF10295 The Brain Tumor Drug
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are included but clinical medical themes are not covered, including conventional medical imaging). Examples include Bayesian optimization for molecular or materials design; machine learning for single cell data