515 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Simons Foundation" uni jobs at University of Sheffield
Sort by
Refine Your Search
-
Listed
-
Country
-
Field
-
protein machinery regulating vesicle docking, priming and fusion will be investigated. Please apply for this project using this link: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying References
-
to produce similar ‘carbon-capture’ plants using gene-editing technology. Please apply for this project using this link: https://www.sheffield.ac.uk/postgraduate/phd/apply/applying Funding Notes Open to Self
-
. First class or upper second 2(i) in a relevant subject. To formally apply for a PhD, you must complete the University's application form using the following link: https://www.sheffield.ac.uk/postgraduate
-
to learn cutting-edge techniques using the University of Sheffield’s world-class research facilities, including microscopy, high-throughput genomics and metabolomics. You will also learn how to integrate
-
. This project will develop responsive manufacturing technology that will have sufficient flexibility to overcome such problems by utilizing intelligent machine learning to control the printing process in real
-
discrete (switched) way. The controller must learn a model of the system while the latter is being controlled. While seemingly straightforward, this raises several technical and theoretical difficulties
-
to apply, please visit https://PLusPortal.PerrettLaver.com quoting reference number 8251. For informal inquiries please contact Thomas Cameron at Thomas.Cameron@perrettlaver.com . The deadline
-
How does a molecule walk? Computer simulations of molecular machines in action School of Mathematical and Physical Sciences PhD Research Project Directly Funded UK Students Prof Sarah Harris, Dr
-
, numerate subject (such as maths and physics), including Matlab programming. Full details of how to apply can be found at the following link: https://www.sheffield.ac.uk/acse/research-degrees/applyphd
-
Effective and Efficient Visual Presentation of Machine Learning Outputs Derived from High-Dimensional Data to Clinicians (S3.5-SMP-Alix) School of Medicine and Population Health PhD Research Project