Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
with the possibility of renewal. This project addresses the high computational and energy costs of Large Language Models (LLMs) by developing more efficient training and inference methods, particularly
-
diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
-
software development, and has leading roles in the ATLAS High Luminosity Large-Hadron Collider (HL-LHC) upgrade (Silicon Strip detector, Liquid Argon calorimeter, and Trigger & Data Acquisition). The group
-
involve conducting large-scale data synthesis projects to better understand the drivers of urban metacommunities and biodiversity patterns. In particular, the lab is interested in understanding the drivers
-
development. Basic Qualifications: A PhD in computer science/engineering, electrical engineering, data science or a related field completed within the last five years. Experience of AI and efficient computing
-
multivariate data analysis. Preferred Qualifications: PhD in Chemistry, Materials Science or Chemical Engineering. Candidates are expected to have a strong background in quantitative structure analysis based
-
high fidelity models of ice crystal icing accretion and shedding, verifying tools using the wealth of unique experimental validation data generated by researchers at the Oxford Thermofluids Institute
-
using experimental data from laboratory setups Validate models through targeted measurements and structured testing procedures Analyse loss mechanisms and system efficiencies under various operating
-
) information-theoretic active learning, and c) capturing uncertainty in deep learning models (including large language models). The successful postholder will hold or be close to the completion of a PhD/DPhil in
-
generating, mobilising, and harvesting “big data” to create a dynamic and agnostic collection of information and deliver a new class of research that will enable a better understanding of the clinical