78 condition-monitoring-machine-learning Postdoctoral positions at University of Oxford in Uk
-
of Engineering Science. The post is funded by EPSRC and is fixed term to the 31st January 2027. A2I explores core challenges in AI and machine learning to enable robots to robustly and effectively operate in complex, real
-
. About the Role The post is funded for 3 years and is based in the Big Data Institute, Old Road Campus. You will join an interdisciplinary team of researchers spanning imaging science, machine learning
-
and decision-making in humans and machine learning systems. The post-holder will have responsibility for carrying out rigorous and impactful research into human-AI interaction and alignment, with a
-
will contribute to the development of a new simulation-based pre-training framework for building more robust and trustworthy machine learning-based clinical prediction models. Funded by the Medical
-
navigation algorithms and machine learning models on physical robot platforms. We are particularly interested in candidates with expertise in generative AI and curriculum learning applied to robotics, as
-
catalytic turbomachines—compact devices that combine chemical reaction and flow functions—using a novel machine-learning-based method, ChemZIP, to accelerate the modelling of complex catalytic and gas-phase
-
will work as a member of an interdisciplinary team (including experts in machine-learning and microbiology) to establish microfluidics-enabled microscopy assays on single bacterial cells to determine
-
, ensuring they are kept fully up to date with progress and difficulties in the research projects. It is essential that you hold a PhD/DPhil in a quantitative discipline (e.g. Statistics, Machine Learning
-
collaborative links thorough our collaborative network. The researcher should have a PhD/DPhil (or be near completion) in robotics, computer vision, machine learning or a closely related field. You have an
-
We are seeking a highly talented and experienced Postdoctoral Researcher to join a research team led by Prof Chris Summerfield focussed on studying learning and decision-making in humans and machine