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Field
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algorithms that maximize the information extracted from images and delivered to the robot. To be successful in this role, we are looking for candidates to have the following skills and experience. We welcome
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responsibilities may include: Development or analysis of novel Machine Learning algorithms for engineering design applications, such as Inverse Design, Surrogate Modeling, or generative modeling. Collaborating with
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reconstruction algorithms. Inconsistencies can be used to correct the input data, for example to improve attenuation correction. The aim of this postdoc is to correct rigid motion in SPECT reconstruction based
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, architectures, and algorithms. Specifically, we investigate energy-efficient computer architecture for machine learning based on novel devices and computing principles, device-aware machine learning algorithms
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sensing, IoT sensors, and climate models. Design and implement deep learning models for forecasting extreme weather events such as floods, droughts, and heatwaves, integrating probabilistic approaches
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sensor data, with applications in disease modeling and the development of material science-based innovations. These efforts aim to optimize system performance and uncover novel biological insights in close
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outperforms previous magnetoresistive devices by orders of magnitude, and ii) assess its potential for applications, including magnetoresistive sensors. Responsibilities and qualifications The primary tasks
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, telecommunications or related field. Other requirements include Strong background in communication theory, signal processing, and wireless communications, Extensive experience in physical (PHY) layer algorithm design
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, complex systems. Strong communication and writing skills; ability to work both independently and as part of a team. About the team The DATA team develops foundational mathematical and algorithmic approaches
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developed by the project partners will be based on two key technologies: machine learning algorithms that generate artificial yet realistic data points (synthetic health data) and secure multi-party