78 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" "St" Postdoctoral positions at University of Oxford
Sort by
Refine Your Search
-
/DPhil in robotics, computer science, machine learning, informatics, AI, or a closely related field. You will have an excellent academic track record in topics relevant to locomotion and manipulation; path
-
carried out in collaboration with Prof. Susan Lea FRS FMedSci at St. Jude Children’s Research Hospital, and the successful candidates will have the opportunity to spend periods at St. Jude in Memphis, USA
-
fundamental research, we create widely used open-source software including autodE, cgbind/C3, and mlp-train. Our recent advances in Machine Learning Interatomic Potentials (MLIPs) form the foundation of our ERC
-
, Engineering, or a closely related discipline. You will be a materials or physical scientist with a strong track record in applying deep learning to computer vision problems, ideally within battery
-
that integrate multi-omics data to uncover mechanisms of disease, cellular resilience, and therapeutic response. The post holder will lead research applying large-scale machine learning and foundation models
-
application of AI and machine learning models to interpret complex X-ray datasets, and the integration of experimental and computational insights to generate actionable knowledge that advances sustainable metal
-
The postdoctoral researcher will lead the development of computational methods for aligning cortical organisation across species using transcriptomic and anatomical data combined with modern machine
-
imaging datasets and advanced machine learning approaches to identify novel imaging markers of mental health disorders and cognitive function; 2) developing robust MRI-based acquisition, image
-
and machine learning systems led by Prof Christopher Summerfield. The post-holder will have responsibility for carrying out rigorous and impactful research into human-AI interaction and alignment, with
-
operational practices • Systematically exploring different formulations of mixed-integer constraints in grid optimisation problems • Developing machine learning models to accelerate mixed-integer