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; EPSRC Centre for Doctoral Training in Green Industrial Futures | Edinburgh, Scotland | United Kingdom | 20 days ago
, further exploring the carbon-related impact of polymer breakdown. The project will also incorporate artificial intelligence (AI) and machine learning to optimise degradation processes and data analysis
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used to develop networks capable of self-learning and self-optimisation, adapting to real-time changes in traffic and demand. The successful candidate will contribute to designing solutions that optimise
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achieve automated data driven optimization (in terms of time and quality) of polishing process parameters by application of machine learning algorithms, leading to a robust, repeatable and fast polishing
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cybersecurity team at Luleå University of Technology and also our partner within Cybercampus Sweden, RISE. We will use methods and technologies including expert systems, evolutionary algorithms, machine learning
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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
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the field of biotechnology, bio-/chemical engineering, (bio) process engineering, bioinformatics, biophysics or biomathematics. Ideally you have Programming skills and knowledge on machine learning and
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Apply now The Faculty of Science and the Leiden Institute of Advanced Computer Science (LIACS) are looking for a: PhD Candidate, Secure Computation Technologies and Applications to Machine Learning
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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science and energy technologies Basic knowledge of artificial intelligence and data analysis methods Programming skills, ideally in Python Independent and analytical way of working Reliable and thorough
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become the bottleneck in achieving optimal performance and trustworthiness. This project will focus on how a federated multi-task learning framework can be effectively designed and optimised to address