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research is essential, as well as the ability to work closely within teams, have a good grasp of using technology (laptops, Virtual Private Networks (VPNs) and Microsoft Excel) and being enthusiastic
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: robotics, smart technologies, software engineering, security and data analytics. You will be contractually enrolled into the Teachers’ Pension Scheme (TPS) – one of the largest and most generous defined
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and FDS is preferred. Candidate preferably should be familiar and have sufficient experience in using ANSYS FLUENT and FDS, Meshing tool and post-processing software. Able to work independently with
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will work closely with the Principal Investigator (PI), Co-PI, and the research team to develop deep learning-based computer vision algorithms and software for object detection, classification, and
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integration, contributing to CSIE’s mission of advancing autonomous multi-agent systems for national defence applications. Key Responsibilities: Design and develop hardware and software platforms for UAV swarm
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to monitor oceanic CO2 uptake with improved confidence. Any future observational network utilises a range of instrument/sensor technologies, deployed on different platforms, and measuring multiple variables
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machine learning/AI or cybersecurity, or both, as evidenced by a strong track record of publications in leading journals and conferences in relevant areas. Software programming skills. To be successful
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activities within the scope of WP6 of this project, contributing to the development of software solutions to support clinical research. 6. Applicable legislation and regulations: Research Fellow Statute, as
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, statistical genetics, genomics, genetic epidemiology, and bioinformatics as well as in-depth knowledge of genomics and relevant software. The candidate will be required to demonstrate relevant research and
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• Proficiency in at least one statistical software (e.g., R, Stata, SPSS, Python) • Expertise in quantitative analysis, with preferred skills in o Quasi-experimental evaluation techniques (e.g., Difference-in