Sort by
Refine Your Search
-
undertaken in a hybrid manner. We are also open to discussing flexible working arrangements. Do you have an interest in high-performance computing (HPC), digital research technologies or Research Software
-
the computational work. If you are looking for a role that will provide new knowledge on a very important membrane protein, apply today. As Research Fellow your main duties will include: Working with and in support
-
Do you have a strong technical background in Corrosion, Machine Learning and Numerical Modelling? Are you interested in working with industry to develop Machine Learning methodologies and protocols
-
Do you have a strong technical background in Machine Learning and Numerical Modelling? Are you interested in working with industry to develop Machine Learning methodologies and protocols needed
-
until 31st March 2027. You should have a degree in a numerate discipline and practical experience of advanced statistical techniques. You will be responsible for analysing existing data, developing
-
have experience developing and running large numerical models and analysing complex datasets? Would you like to work with a world-leading team at Leeds to connect molecular-scale insight to cloud-scale
-
CSIRO and numerous industry partners in the UK and across the globe. You will have a PhD or near completion (i.e., initial thesis to be submitted before the start date) in Computer Science, Software
-
approaches. You will provide expert guidance, training and advice in areas covering sample preparation, optimisation, method development, data acquisition, and data interpretation. You should have, or be
-
allocation and system performance under uncertainty. This includes formulating complex decision problems, designing deterministic and real‑time solution methods, and contributing to high‑quality academic
-
@leeds.ac.uk Project summary The project focuses on developing new statistical methods for detecting unusual patterns in healthcare-associated infections. This is a fully funded 3.5-year PhD project supported by