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Field
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. To perform any other duties related to the research program. Job Requirements: Preferably PhD degree in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly
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qualifications: Experience with implementation or applications of large machine learning models Experience with generative methods for protein design and/or docking simulations or generative methods
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, and how their combination can improve safety signal detection. As a PhD fellow, you will be working with large-scale longitudinal data, managing data, writing scripts, performing statistical analyses
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publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
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, United States of America [map ] Subject Areas: Electrical and Computer Engineering / artificial intelligence , Artificial Intelligence and Machine Learning (AI/ML) Starting Date: 2026/01/01 Salary Range: $62,232-$80,000
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results in foundational neural models, where models learn from large unlabelled image datasets, but also on additional data like clinical reports or electronic health rec-ords. The work will be done in
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application development. Deep Learning techniques, Data Engineering, and Semantic Technologies Open-source artificial intelligence, machine learning, statistical estimation methods, software tools, and big-data
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large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. Alongside developing their own research ideas, applicants should be capable
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statistical and machine learning modeling to conduct data analyses for large-scale multimodal (genomics, omics etc) studies. Conceptualise new ideas, lead data-driven discoveries, ensuring in-depth assessment
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a possible extension for one more year. The starting date is November or December 2025. This post will advance the application of Machine Learning (ML) in weather forecasting and hydrological