29 algorithm-development-"Multiple"-"Embry-Riddle-Aeronautical-University" positions in United Kingdom
Sort by
Refine Your Search
-
Category
-
Program
-
Employer
- ;
- Cranfield University
- ; The University of Manchester
- Imperial College London
- KINGS COLLEGE LONDON
- UNIVERSITY OF SOUTHAMPTON
- University of Sheffield
- ; King's College London
- ; Newcastle University
- ; University of Sheffield
- ; University of Southampton
- AALTO UNIVERSITY
- University of Birmingham
- University of Bristol
- University of Glasgow
- University of Oxford
- 6 more »
- « less
-
Field
-
input needs, accompanied by a boost in algorithmic development, e.g., multi-modal learning, transfer learning, federate learning, and knowledge embedding, etc. However, a significant motivation of
-
control laws into Trent gas turbine engines and developed algorithms monitoring fleets of 100s of engines flying all around the world. During the PhD, you will have the opportunity to deeply engage with
-
development, human-computer interaction, data analytics, user experience design, remote monitoring systems, energy optimization algorithms, and environmental impact modeling. Human-centric AI-driven sanitation
-
for Artificial Intelligence (FCAI), ELLIS Institute Finland, and Aalto University House of AI, invites applications for multiple postdoctoral positions. Our team works actively to develop intelligent robotic
-
independent higher education provider, offering flexible and inclusive learning across multiple London campuses. We are student focused, digitally forward, and committed to academic excellence reflected in our
-
Innovation (UKRI), focusing on populations with multiple long-term conditions. You will contribute to a social care initiative, developing and testing an AI-informed digital tool to help individuals with
-
tuition fees. This PhD project in the area of autonomy, navigation and artificial intelligence, aims to advance the development of intelligent and resilient navigation systems for autonomous transport
-
integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
-
learning (ML) for high-fidelity data ‘stitching’. The integration of data from multiple analytical platforms is critical for advancing the understanding of complex biological and chemical systems. This work
-
are critical especially around congested or critical infrastructures. This research aims to develop decision making and planning algorithms that can mitigate the risks challenging environments of AAM