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-approved systems. Ensure adherence to data security and compliance protocols. Preferred Qualifications MSc or PhD in Bioinformatics, Computational Biology, Computer Science, or a related field. Strong
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partners across Europe to deliver a world-class doctoral training programme in risk assessment, resilience engineering, and smart technologies. Its scientific vision targets: (1) the development of a
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through their investor conference a co-investment from the College of Science and Engineering. This funding provides a unique opportunity to advance innovative medical device research and support four high
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and multidisciplinary team. The successful candidate should have completed an MSc degree in ecology or environmental sciences (minimum grade B) and have documented skills in ecology (especially invasive
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Current reseach is in the areas of: Development of biomimetic structures as ultrasound contrast agents Deep tissue imaging using photoacoustic contrast agents All optical photoacoustic sensors for tomagraphic imaging in tissue Neural network correction of distortions in acoustic transducers web...
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Contract: Full Time/Fixed Term for three (3) years.Supervisor:Dr Mohamed Lamine Faycal BELLAREDJ, Assistant ProfessorDepartment of electrical engineering, Universite de Moncton (Moncton campus)Email
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Max Planck Institute for Demographic Research (MPIDR) | Rostock, Mecklenburg Vorpommern | Germany | 28 days ago
Assistant position, with 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
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of bioinformatics, biology, or related fields - M.Sc. is required. Key qualifications and qualities include: Experience in bioinformatics/computational biology; experience in protein structure prediction is
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computer science and information system engineering while collaborating in a publicly funded research project with our industry partner Encevo. In this project, the aim is to analyze and build a robust anonymization
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members of staff. Research in the Department is organised into six themes : Causality; Computational Statistics and Machine Learning; Economics, Finance and Business; Environmental Statistics; Probability