Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
particular focus on the effects of boundary layer ingestion. The work also includes: Whole-aircraft noise prediction Flight trajectory optimization Environmental and societal impact assessment of future air
-
potential applications. In particular, we focus on evolutionary prediction: can we use a deeper understanding of evolvability to predict and, potentially, control evolutionary processes? Read more about our
-
. This is a relatively unexplored area, especially when it comes to combining different measurement scales and determining the minimum level of detail required for accurate predictions of wood properties
-
. Project description Characteristic research topics will focus on the following key areas Collaborative intelligent Autonomy - Predictive collaboration in shared workspaces - Distributed task and motion
-
neglect critical lake-specific dynamics and feedback mechanisms that are essential for accurately predicting ecological responses and climate interactions. Lake physical dynamics is crucial for ecosystem
-
The Rantalainen group is focused on application of machine learning and AI for development and validation of predictive models for cancer precision medicine, with a particular focus computational pathology. Our
-
of bioadhesives to better predict and optimise the production process and product properties, and thus ultimately expects to enhance the efficiency of the production process in different wood and fibre-based
-
well as contributing to the development of predictive in vitro models for hazard identification. Additional duties include sample collection and characterization of airborne particle emissions at industrial sites
-
biochemical and OMICs technologies to understand and predict the role of foods, dietary components and dietary patterns in human health. The research is also related to global food security topics in
-
applications, specifically targeting the prognosis and risk prediction of Heart Failure (HF) in patients. This research integrates AI safety, explainability, and multimodal medical data analysis to enhance