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supervision of Thaleia Dimitra Doudali. The specific topic of the research will be determined based on the common interests of the candidate and the supervisor. Here are some potential topics: [Machine Learning
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machine learning offer a great opportunity to effectively extract evidence that will need to be calibrated in the form of a LR for presentation in court. Some members of the network, including the Ph. D
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of the training process of machine learning algorithms for power system applications 2) Enhance the performance and scalability of a peer-to-peer trading optimization algorithm using quantum computing (QC). Develop
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of machine learning and bioinformatics, implementing advanced techniques such as encoder-decoder language models, graph neural networks, and contrastive learning to annotate protein sequences with precision
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motivated and eager to work on evolutionary and biological problems and to learn about the use and development of computational, statistical and machine learning approaches. The Group Cancer is a genetic
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of hyperactivity. UA Institute for Computer Research” and “Smart machine learning for business modelling and analysis. Department of Software and Computing Systems.” Reference: I-PI 37-24 Funding agency: Valencia
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learning of generative protein language models. The successful candidate will have the unique opportunity to work at the interface of computational and experimental biology, implementing and refining machine
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skills. Excellent academic record. International experience. Previous experience with experiments Experience in modelling, programming, and machine learning. Excellent English speaking and writing skills
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, or equivalent to the First Certificate. Ability to communicate verbally and in writing in Catalan and Spanish. Knowledge of homomorphic encryption, multi-party computing, machine learning, MATLAB and Python
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for this PhD position should have a solid understanding of data science and a keen interest in environmental applications. Basic knowledge of statistical analysis and some experience with machine learning and