557 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Univ" uni jobs at University of Sheffield in United Kingdom
Sort by
Refine Your Search
-
Listed
-
Field
-
EngD: Enhancing productivity in manufacturing through automation and autonomy in computer aided process planning (sponsored by Boeing) EPSRC Centre for Doctoral Training in Machining, Assembly, and
-
: https://www.sheffield.ac.uk/postgraduate/phd/apply/english-language. How to apply: Please see this link for information on how to apply: https://www.sheffield.ac.uk/cbe/postgraduate/phd/how-apply. Please
-
per week. Duration: Open Ended Line manager: Operations Manager Direct reports: N/A Our website: https://www.sheffield.ac.uk/mps For informal enquiries about this job contact Kirsty Wallace, Staffing
-
combination of physics-based, and data-driven AI-based approaches employing neural-networks and machine learning, this project will develop and validate a multi-time scale DT concept for advanced condition
-
, the integration of work-related learning in taught programmes, and expanded placement year participation. The placement year opportunity as an Employability Assistant will work across faculty-facing employability
-
, increased carbon dioxide, rainfall and snow regime change and how these impact the biodiversity of ecosystems, and the capacity of ecosystems to cycle carbon and nutrients (https://sites.google.com/a
-
at the University of Sheffield. There will be many opportunities to collaborate with ongoing work in the lab. For more details see http://www.alisonewright.co.uk. Applicants are strongly encouraged to contact Dr
-
website https://www.sheffield.ac.uk/eee For informal enquiries about this job contact Professor Mahnaz Arvaneh, on m.arvaneh@sheffield.ac.uk Next steps in the recruitment process It is anticipated
-
the University of Sheffield online application portal for postgraduate research in Chemistry: https://www.sheffield.ac.uk/postgraduate/phd/apply When completing your application, please specify Dr Marco Conte as
-
, the project proposes to also use machine learning techniques to learn parts of the prior and penalty structure from data in an interpretable way. Examples include mapping liquidity and volatility features to a