Sort by
Refine Your Search
-
for the Research Associate, Grade 7 level, position must have a PhD in a quantitative biology discipline, statistics or machine learning along with a proven track record of research using statistical modelling
-
that require treatment while reducing unnecessary detection of slow-growing cancers. You will play a key role in a large mixed methods project as a qualitative researcher conducting focus groups, interviews and
-
research associates and PhD students. You will also engage directly with numerous external stakeholders (industry and academia), which will form part of your research network and give you opportunities
-
Overview This research assistant post will enable the candidate to undertake a PhD on the diagnosis of pancreatic cancer, specifically focusing on examining the use of imaging before a diagnosis is
-
this emerging research programme Applicants should be familiar with methods for estimating comparative effectiveness using RWD, e.g., NICE’s TSD 17 , NICE’s RWE framework . You will be encouraged to develop your
-
contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine
-
development, excellent time management skills and who is able to work on their own initiative, working methodically and accurately to follow procedures and instructions. Main duties and responsibilities First
-
opportunities to apply for independent research funding or to develop proposals for a laboratory, clinical or translational research proposal for a higher degree (PhD/MD). Main duties and responsibilities
-
contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine
-
contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine