Sort by
Refine Your Search
-
Listed
-
Country
-
Field
-
to underfill at Grade 6 (£34,982 - £40,855 p.a.) if candidate holds a relevant degree and is working on PhD/DPhil) together with established knowledge in computer architecture and hardware security, significant
-
About the Role We are seeking an enthusiastic and motivated postdoctoral researcher to apply advanced data analytics and machine learning techniques to real-world clinical data in the field of viral
-
energy densities exceeding LMFP and competitive with NMC. A postdoctoral research position is available on this 3D-CAT project in the area of computer modelling and materials design of lithium battery
-
funded by UKRI EPSRC and is fixed term for 12 months. You will be contributing to joint UKRI EPSRC – NSF CBET project on sustainable computer networks, with a focus on carbon emissions reduction and
-
Applications are invited for a Postdoctoral Research Assistant in Data processing for the MIGHTEE survey. This is a senior role funded through the UKRI Frontier Research Grant of Prof. Matthew
-
attached to the project. The successful applicant must hold a PhD/DPhil in a relevant subject. They must have peer-reviewed publications using data science approaches, for example, genetic analysis
-
of healthy aging using cutting-edge single-cell and AI-based tools, as well as population cohort data. You will be an integral member of the Awadalla lab, working with some of the world’s largest single-cell
-
prepare the work for publication. You will have a PhD/DPhil in health modelling, or related subject such as health economics or public health. You will need strong data analysis skills (such as in STATA and
-
hold, or are close to completing, a PhD in robotics, robot learning, or a closely related field. You possess strong expertise in deep learning and robot navigation, with hands-on experience in deploying
-
simulations, generalize experimental observations, and offer insight on the response of selected case studies. You should hold a PhD/DPhil (or be near completion) in numerical modelling for geotechnical