20 condition-monitoring-machine-learning Fellowship positions at UNIVERSITY OF SOUTHAMPTON
Sort by
Refine Your Search
-
Are you keen to pioneer machine learning models that address the challenges of robot perception? We are recruiting a research fellow who will work on our EPSRC-funded research project on “Active
-
modelling, satellite data assimilation, multivariate statistics, and machine learning. Prior experience with model and satellite products for mapping and understanding SM-dependent hazards (like floods
-
or more of: the use of micro/nanofabrication and materials characterization tools; computational multi-physics/electromagnetics modelling and/or the application of machine learning algorithms; experimental
-
: Genomics, precision medicine, bioengineering, and health data science AI and Digital: Machine learning, robotics, digital health, and cybersecurity Defence and Advanced Manufacturing: Secure systems
-
manufacturing. You should have interest in or experience with data-driven methods, including machine learning, Python programming, or data curation. Regular reports of research progress are required and research
-
conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences
-
-world conditions to verify system operation against targets and demonstrate the reliability of the technology for use in backup power, grid stabilisation, and renewable energy integration applications
-
in distributed database systems, information retrieval, computer networking or semantic web. The post does not involve working outside of the UK for over 30 days in a row or over 90 days in a year. For
-
bioelectronic system that seamlessly integrates therapeutic functions with continuous monitoring capabilities. The system incorporates cutting-edge technologies in bioelectronics, soft robotics, and materials
-
conditions. Our work combines traditional statistical methods with advanced artificial intelligence algorithms to identify patterns in disease. We also use qualitative methods to understand lived experiences