43 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" uni jobs at University of Sheffield
Sort by
Refine Your Search
-
Machine Learning techniques. As Research Associate you will have a research leadership role in the group, and will assist in day-to-day supervision of post-graduate research students. You will collaborate
-
of the resultant analysis. This will also involve the collection of a curated data set, and the use of Machine learning tools to further enhance the analytical process. Together, this will help expand the use
-
specialised expertise in the Machine Learning for Engineering sub-theme. Candidates from all areas in machine learning are encouraged to apply, with a special focus on the areas of (i) information theory and
-
with core data analysis and professional skills that are necessary for bioscience research and related non-academic careers. https://www.yorkshirebiosciencedtp.ac.uk Project Description: Join an exciting
-
Accessible Tinnitus Notch Noise Therapy via Machine Learning, Acoustic Metamaterials and Additive Manufacturing (with NHS and TinnitusUK) EPSRC Centre for Doctoral Training in Sustainable Sound
-
Physics based machine learning algorithm to assess the onset of amplitude modulation in wind turbine noise (with TNEI Group) EPSRC Centre for Doctoral Training in Sustainable Sound Futures PhD
-
methods and the potential to develop pioneering reconstruction and calibration techniques involving machine learning. The PhD will prepare equally well for a career in industry and academia. The candidate
-
of dark matter particles in the decay of the gluinos. The search will be performed using the full ATLAS Run-3 13.6 TeV proton-proton collision data. The student will gain expertise in machine learning and
-
survival is shaped by resource availability, competition, and cultural knowledge of food resources. You will work with an established bioacoustic dataset, a validated call machine-learning classifier, and a
-
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