594 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions in United Kingdom
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About the Role You will develop and apply novel computational methods to quantify the societal impact of fundamental science discoveries. Candidates close to completion of their PhD will initially
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between Departments is important to the role which will be based in the Materials Innovation Factory. The post will involve leading an iterative programme of applied polymer chemistry and materials science
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Travel ” which examines signal processing and machine learning methods for inferring active travel activities from optical fibre signals. About You Applicants must have an Undergraduate Degree in
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. Familiarity with standard design verification (DV) procedures and continuous integration (CI) setups would be beneficial. Knowledge of machine learning workloads and the design of machine-learning accelerators
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United Kingdom Application Deadline 12 Sep 2025 - 00:00 (UTC) Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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scholars in Law and two PhD students (one in Law and one in Computer Science/Data Analytics), as well as with international, European and national stakeholders involved in the CURE project. The post-holder
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operation · Application of artificial intelligence or machine learning in energy or engineering systems 5. Strong programming and modelling skills using relevant tools such as Python, MATLAB
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vacuum systems. Experience of programming for data acquisition and analysis (preferably in Python). Experience of computer aided design (CAD) of components and devices. Experience in writing numerical
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2029. About you Essential criteria PhD in developmental cognitive computational science, neuroscience, computer science, or related disciplines*. Previous experience in social cognitive research
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their relationship with treatment responses and disease activity. This is an excellent opportunity to contribute to a collaborative research program dedicated to improving our understanding of ALS and informing future