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Field
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees
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-Reflective Sensor Oculography (PSOG), addressing calibration and cross-user generalization challenges through the use of hypernetworks. PSOG signals are highly sensitive to geometric, anatomical, and
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parametric winglet geometry model. The developed tool is meant to be able to generate winglet shapes, for a given wing planform, using a set of geometric parameters, which can be incorporated in
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of manufactured parts, particularly in terms of geometrical accuracy, microstructural control, and mechanical performance. This PhD project aims to develop a Multiphysics model and simulation to gain in-depth
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incorporate it into mathematical models of trait evolution across phylogenies. The work combines dimensionality reduction and geometric data analysis with the development of statistically rigorous comparative
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Omer Ben-Neria Logic, set theory Shai Evra Graph theory, representation theory, number theory Adi Glucksam Complex analysis, potential theory, and dynamics Or Hershkovits Geometric analysis
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. The work may include inverse problems, regularization strategies, statistical modeling, representation learning, and geometric or variational approaches to volumetric data. There is substantial freedom
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variability. The work may include inverse problems, regularization strategies, statistical modeling, representation learning, and geometric or variational approaches to volumetric data. There is substantial
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fellow in PHYsics-based modelling towards Geometric Algebra Transformers for ISAC. This exciting role will require the successful candidate to develop a physics-informed framework for reconstructing
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novel machine learning models—including Physics-Informed Neural Networks (PINNs), variational autoencoders, and geometric deep learning—to fuse multimodal data from diverse experimental probes like Bragg