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Field
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analytical skills for model formulation and optimization Demonstrated research potential, ideally with a track record of publications in relevant venues (journals such as IEEE T-ITS, INFORMS Transportation
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and accepted to the PhD program at Stockholm University. Project description Project title: “Deep learning modeling of spatial biology data for expression profile-based drug repurposing”. A new exciting
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Martin Australia invite applications for a project under this program, advancing robotic perception systems through monitoring of their machine learning models. Run-Time Monitoring of Machine Learning
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to model and analyse the intrinsic complexities of these systems. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains
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exposome and dynamic exposome modeling, learning in timeseries and spatial data, and hybrid deep learning-causal modeling. The successful applicant should have significant research experience in at least two
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emerging types of national emergencies and evaluate their spatial and operational implications. This will include an analysis of UK population distributions, terrain, infrastructure access, and airspace
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“Cellular Plasticity in Myeloid Malignancies: From Mechanisms to Therapies”. In this CRC we will focus on myeloid malignancies as a model to dissect the various molecular mechanisms that enable and regulate
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softness with mechanical performance. Compliant mechanisms, particularly spatial compliant shell mechanisms, offer a promising solution. These structures can achieve large, controlled deformations while
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framework exploiting the use of physical and geometrical conservation laws in a variety of spatial discretisation schemes (i.e. Finite Element, Finite Volume, Meshless). The resulting conservation-type
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, however, these may not always be able to provide the spatial and/or temporal coverage that is required. In such cases, information from ground measurements can be combined with information from other