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Kontogianni. Our research explores how intelligent systems can perceive, understand, and interact with the 3D world. We develop new methods in computer vision, machine learning, and multimodal 3D
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Computer Vision There is growing trend towards explainable AI (XAI) today. Opaque-box models with deep learning (DL) offer high accuracy but are not explainable due to which there can be problems in
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Biological Learning Machine, which is headed by Professor Jan Østergaard. The goal is to develop novel information-theoretic methods for identifying and analyzing temporal and spatial patterns of synergy and
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: Operator Algebras, Machine Learning, Analytic Number Theory, Automorphic Forms and Representation Theory Appl Deadline: 2025/10/10 11:59PM (posted 2025/09/10, listed until 2025/10/10) Position Description
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work, among other things on song learning in songbirds, hearing in frogs and bats and the effects of anthropogenic noise in marine mammals. Our research uses a number of methods within physiology
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competencies The applicant must hold a master’s degree in engineering and a PhD in a relevant field, such as electrical engineering, with expertise in physics-based modeling, machine learning, and optimization
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employ cutting-edge single-cell and spatial omics technologies with bioinformatics and machine learning to decipher principles of gene regulation underlying cell identity and its disruption in human
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technology areas such as cybersecurity and AI. Complex digital technologies such as advanced cryptographic techniques, machine learning, and mixed reality increasingly influence the lives of children. However
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Algorithms (APX). Big Data Systems (BDS). Computational Methods in Simulation (CMIS). Machine Learning B (MLB). Extended Reality. Proactive Computer Security (PCS). Program Analysis and Transformation (PAT