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Foundation RECRUIT grant ("Data Management, Algorithms, & Machine Learning for Emerging Problems in Large Networks – with Interdisciplinary Applications in Life & Health Sciences". NNF22OC0072415
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metagenomics and Oxford Nanopore Technologies sequencing. Large experience in bioinformatics, machine learning and high-performance computing in relation to microbial metagenomics and analysis of horizontal gene
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metagenomics and Oxford Nanopore Technologies sequencing, wet lab method development and DNA library preparation for Oxford Nanopore sequencing. Large experience in bioinformatics, machine learning and high
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, bioinformatics, aging biology, epidimological data and AI-driven systems modeling. The successful candidate will develop and apply computational and machine learning approaches to decode the molecular and
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academic or industry leadership roles. Your profile Applicants should hold a PhD in Computer Science, Electrical Engineering, Computer Engineering, Telecommunications, or a similar field, with a strong
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intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and
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, glacier speedup, and ice-ocean interaction. Candidates will work with satellite altimetry, velocity datasets, and climate data to quantify ice sheet mass balance and dynamics. Applicants should hold a PhD
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Post Doctoral Researcher in Human-centred Large Language Models for Software Engineering, Departm...
The Section for Software Engineering and Computing Systems, at the Department of Electrical and Computer Engineering (ECE), invites applicants for a two-year postdoctoral position within the area of
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algorithms for speech enhancement using state-of-the-art machine learning techniques. You will design and evaluate models that leverage phoneme-level or discrete speech representations and conduct experiments
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predictive framework linking genomic data to extinction risk, working at the interface of evolutionary genomics, simulation modelling, and machine learning. By integrating forward-in-time simulations, real