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approaches for using machine learning to analyze X-ray data, particularly Resonant Inelastic X-ray Scattering (RIXS). The position will collaborate with experts in RIXS experiments (Mark Dean), computational
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large-scale speech and wearable data from participants in the GLAD Study cohort ( https://gladstudy.org.uk/ ). Using large language models (LLMs) and acoustic analytics, they will uncover patterns in
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catalysts for the synthesis of a range of industrially valuable compounds. This PhD project is part of the Horizon Europe Marie Sklodowska-Curie Action (MSCA) doctoral network (DN) ELEGANCE (machinE LEarning
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considered for this role, you should have: A PhD (or equivalent) in a relevant discipline (e.g., biostatistics, machine learning, computer science, clinical informatics, natural language processing). Strong
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experiments, behavioral research, econometric and causal inference approaches, optimization and analytical modeling, and data-driven techniques such as machine learning and large language models. Our work is
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qualifications PhD in Microbiology, Computational Biology, Genomics, Systems Biology, Statistics, Machine Learning or related discipline Publication record in Microbiology, Metagenomics, Metabolic/Statistical
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, or a related field. Proven experience in machine learning, deep learning, generative AI and data mining. Strong programming skills (e.g., Python, R, MATLAB, or similar). Experience with data
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, researchers and other staff. Experience with streaming infrastructure (e.g., Apache Kafka, ActiveMQ), real-time data processing frameworks (such as Apache Flink or Spark Streaming), and machine learning is
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sociology. Strong quantitative skills and experience with large-scale data analysis required. Computer Science/HCI: PhD in Computer Science, Human-Computer Interaction, Information Science, or related