Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
- University of Oxford
- University of Oxford;
- AALTO UNIVERSITY
- Durham University
- King's College London
- University of Liverpool
- Aston University
- Queen Mary University of London;
- UNIVERSITY OF VIENNA
- Nature Careers
- Plymouth University
- The University of Edinburgh;
- University of Bath
- University of Cambridge;
- ;
- Cardiff University
- DURHAM UNIVERSITY
- Durham University;
- Heriot Watt University
- Heriot-Watt University;
- Imperial College London
- King's College London;
- SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
- University of Canterbury, New Zealand;
- University of Lincoln
- University of Lincoln;
- University of Liverpool;
- University of London
- University of Nottingham
- University of York;
- 20 more »
- « less
-
Field
-
asynchronous AI-led chemical optimisation across chemistry laboratories¿. This role sits at the intersection of robotics, machine learning, and chemistry, aiming to develop robotic systems that work
-
predictive control of carbon mineralisation through high-throughput mineralogy and machine learning.” This is an exciting opportunity to contribute to innovative research at the interface of mineralogy
-
Engineering, Mathematics, Statistics, Computer Science or conjugate subject and have a strong record of publication in the relevant literature. Good knowledge of machine learning algorithms is essential, as
-
willingness to become familiar with the other, as well as multi-agent systems and ontologies. 4. Excellent Python programming skills, and familiarity with standard machine learning libraries. 5
-
/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
-
or statistical machine learning and have the ability to manage own academic research and associated activities. You will also have a commitment to demonstrating respect, courtesy and consideration within
-
analyses of ant-collected honeydew; and hyperspectral imaging with machine learning to remotely quantify aphid density and physiological state. The results will be field-validated in the context
-
machine learning methods to model changes in the brain over the lifespan, including brain structure and function, and how those changes relate to environment and genomics. What We Offer As an employer, we
-
year-long module performance in the water industry; (ii) exploring whether machine learning, couple with transport informed models can be used to predict membrane fouling for specific applications
-
using liquid biopsy next generation sequencing data for cancer diagnostics. About You Must have a strong background in next generation sequencing data analysis/machine learning, cancer and/or genome