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Biomedical Campus. You will join an exciting research programme investigating fundamental mechanisms of ribosome assembly, translational control and how defects in these processes drive cancer development
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technicians. Applicants should have experience in experimental fluid mechanics and aerodynamics. Prior experience in aeroacoustics is desirable. Applications are welcome from candidates currently pursuing
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machine learning. The position will involve working with different research groups in the Department of Computer Science at the University of Cambridge, UK. In this collaborative project, we will apply
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of tomorrow and creating novel solutions to major global challenges. Our community is made up of 13 000 students, 400 professors and close to 4 500 other faculty and staff working on our dynamic campus in Espoo
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, and dynamic individual who is a team worker, has a positive outlook, and is adaptable and flexible in their working methods. It is also essential that you are highly experienced in setting up continuous
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the successful candidate embarking on a PhD programme at LSHTM. It is anticipated that the role will lead to a further 18 month funded opportunity at Max Planck Institute for Demographic Research (MPIDR), in
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development and refinement accordingly. We are looking for a highly organised, driven, and dynamic individual who is a team worker, has a positive outlook, and is adaptable and flexible in their working methods
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your academic profile in a friendly, supportive, and dynamic centre, with the possibility of contributing to scholarly and policy publications, research seminars and transitioning into a PhD and/or
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expertise or additional resource for a project.) You will contribute to a programme of research, funded by the Horne Family Charitable Trust, which aims to increase the evidence base on Domestic Violence and
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the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity