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will then develop XR applications that provide real-time guidance through visual, auditory, or haptic feedback in everyday situations. Using wearable devices, machine learning, and cognitive models you
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limited available. In this PhD project, you will help develop the first blood-based test for ANOCA, combining next generation sequencing, multi-omics analyses, and advanced machine learning to diagnose
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of the Digital Twin, from architecture design and machine-learning calibration to validation against experimental data. This job offer is part of the Horizon Europe project AIM. AIM (AI-Multiscale Integration
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This PhD project, part of the REACT MSCA Doctoral Network, aims to develop an energy-efficient compute-in-memory (CIM) architecture using gain-cell memory for real-time edge learning, addressing
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computer vision, deep learning, and logical reconstruction techniques. The research investigates how multimodal imaging modalities - including scanning electron microscopy (SEM), photon emission microscopy
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) with computational methods. The candidate will obtain single-molecule multiplexing data and validate machine learning predictions using the high-throughput data. The successful candidate will collaborate
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researchers from diverse backgrounds. Good communication skills and a willingness to learn are important for working effectively within and beyond the consortium. Candidates should hold a Master’s degree in
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modeling, geometric deep learning or physics-informed machine learning, or you are willing to learn these quickly; strong collaboration skills: you enjoy working in a multidisciplinary team and feel
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questions. The candidate should be organized, curious, analytically rigorous and comfortable learning unfamiliar methods. Good written and spoken English is essential. Research motivation: a clear interest in
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machine-learning calibration to validation against experimental data. This job offer is part of the Horizon Europe project AIM. AIM (AI-Multiscale Integration for Waste-to-Value Digital Twins) is a four