538 algorithm-"Multiple"-"Prof"-"U.S" "NTNU Norwegian University of Science and Technology" Fellowship positions
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University of Singapore is dedicated to the interdisciplinary study of humans and algorithms on the Internet, and its implications on the society of the future. This is an exciting opportunity to join us as a
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methodology development as well as applied cancer bioinformatics in a variety of disease sites, including the incorporation of statistical, machine learning & QML ideas. Multiple collaborative opportunities
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, including autonomous robotic systems for In-space and On-orbit Servicing, Assembly, and Manufacturing (ISAM and OSAM) and space logistics. Develop and implement control algorithms for distributed multi-agent
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research that covers the energy value chain from generation to innovative end-use solutions, motivated by industrialisation and deployment. ERI@N has multiple Interdisciplinary Research Programmes which
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with rare, resistant, or genomically un-targetable cancers. Responsibilities Design and evaluate algorithms for treatment and response matching using integrated clinical and molecular datasets. Develop
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of microbial genomics and develop computational tools with broad applications in biotechnology and biosecurity. The role involves collaboration with leading researchers across multiple institutions and will
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emphasis on cancers that affect the Asian community in general We are looking for a highly motivated and dedicated researcher with a strong ability to manage and contribute to multiple international projects
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research purposes Good communication and presentation skills, fluency in multiple languages is a plus We regret that only shortlisted candidates will be notified. Hiring Institution: NTU
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capacity-building efforts and stakeholder engagement such as organising and coordinating events, workshops, meetings, webinars, etc. • Support the delivery and execution of programme work across multiple
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the prevalence and risk of modern slavery. There will be a focus on Bayesian nonparametric methods and practical development of MCMC algorithms that can be applied to data. Translating the project findings