30 machine-learning "https:" "https:" "https:" Fellowship research jobs in United Kingdom
Sort by
Refine Your Search
-
Listed
-
Employer
- University of Manchester
- University of Nottingham
- UCL;
- King's College London
- UNIVERSITY OF SOUTHAMPTON
- UNIVERSITY OF SURREY
- University of Leeds
- AALTO UNIVERSITY
- Cranfield University
- Nature Careers
- Queen's University Belfast
- The University of Manchester;
- University College London
- University of Aberdeen;
- University of Sheffield
- 5 more »
- « less
-
Field
-
candidates will have specialist knowledge in signal processing and algorithm design, with experience in machine learning, AI system development and reinforcement learning along with a strong publication record
-
proficiency in Python (e.g., NumPy, Pandas, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with supervised
-
of manufacturing. We have identified an opportunity to combine continuous microfluidic (µF) process models, process analytical techn ology (PAT) and machine learning (ML) to achieve a paradigm shift in bioprocess
-
methods and statistics. Specialist skills in bronchoscopy, EBUS and teaching are desirable with a willingness to learn new skills. The post holder will be required to work independently and as part of a
-
years and in the relevant areas of Machine Learning / Artificial Intelligence, Credit Risk Modeling and Operations Optimization Modeling; The candidate must have strong programming skills in Python, and
-
component disciplines; in explainable multi-modal deep learning models, in causal statistical models and in human-machine teaming and AI ethics. The researcher will conduct internationally-leading research in
-
. Expertise in artificial intelligence and machine learning. Recent research experience in the development of first-principle wave models. Recent research experience in the development of numerical codes
-
modelling, machine learning, growth mixture modelling). Excellent skills in statistics and advanced quantitative data analysis, including strong skills in command driven programming languages (e.g., STATA, R
-
for MND which could be translated into the clinic. The idea is to use cutting edge machine learning to create clinically actionable predictions such as the time from diagnosis to requirement for a
-
environments will provide the successful candidate with opportunities to learn from a large network of talented professionals. Prof. Mariam Jamal-Hanjani is Principal Investigator of the TRACERx study at UCL