557 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "Mines Paris PSL" positions at University of Sheffield
Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
. Essential Interview Manage own time when working on several projects simultaneously, with an ability to prioritise and complete urgent fixes as they occur. Essential Interview Ability to learn new
-
/Interview Further Information Grade 7 Salary £38,783 - £41,064 Work arrangement Full-time Duration March 2026 – March 2030 Line manager Academic Line Manager Our website https://www.sheffield.ac.uk
-
Work arrangement Full-time Duration The project must be completed by 30 July 2026. Line manager Professor of Physical Chemistry Direct reports N/A Our website https://sheffield.ac.uk/mps For informal
-
data analysis and professional skills that are necessary for bioscience research and related non-academic careers. https://www.yorkshirebiosciencedtp.ac.uk About the Project: Parasitic weeds such as
-
opportunities available; and details of our recruitment process, can be found at https://accedtp.ac.uk/, in the ‘prospective applicants’ tab. Project overview Sexual selection plays a key role in shaping traits
-
for MND which could be translated into the clinic. The idea is to use cutting edge machine learning to create clinically actionable predictions such as the time from diagnosis to requirement for a
-
across the University estate.This will be an exciting period within the team with opportunities to learn, develop and get hands on with enterprise networking technologies. Main duties and responsibilities
-
Combining Experimental and Computational Biophysics to Understand the 'On/Off' Switches of Cellular Machines (C3.5-MPS-Ciani) School of Mathematical and Physical Sciences PhD Research Project
-
areas as well as equip you with core data analysis and professional skills that are necessary for bioscience research and related non-academic careers. https://www.yorkshirebiosciencedtp.ac.uk Project
-
device health status through condition monitoring. AI techniques such as machine learning will be used to optimise gate driver performance and to map gate drive signal attributes to power device health