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them. This involves dynamic engagement like walking, observing, and handling objects. The relationship between perceptual experiences and the dynamic structures of the multi-sensorial information
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for health policy decision-making, these methods will be developed using a Bayesian framework. This PhD project will deliver a substantial contribution to original research in the area of health data science
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object detection, instance segmentation and activity recognition. The position is also embedded in the European ELLIS Network of Excellence in AI. We also anticipate regular visits to project partner TNO
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impacts on animal performance. Early detection of these diseases remains a challenge, as it still relies heavily on human observation, which is time-consuming and prone to error. Technologies such as
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may also be appropriate for research projects requiring long-term strategies of building trust to gain access to the object of research. Fieldwork may consist of deep immersion in one place or research
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biochemistry, structural biology, proteomics, novel detection and screening methods and pre-clinical models for wound healing and regenerative medicine applications. REMOD-HEALING is a Doctoral Network funded by
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ethnomycology or ethnobiology large-scale (ethnographic) database construction phylogenetic comparative analyses with Bayesian computational tools The applicant must have the ability to work independently and in
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contribute to cutting-edge research aimed at combating implant-associated infections. Objectives: This PhD project aims to optimise infection prophylaxis and management strategies in hip and knee arthroplasty
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processes related to carbon cycling in the soil-plant system Experience with Bayesian inference and machine learning is an asset Ability to work independently and cooperatively as part of an interdisciplinary
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perception of object shape and its encoding in languages across the world. The successful candidate will work with Prof. Larissa Samuelson on a longitudinal study tracking how children from 24- to 42-months