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Field
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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(MERCE). The main objective is to develop safe planning and reinforcement learning algorithms with various degrees of confidence for variants of Markov decision processes. More precisely, we will develop
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validation (V&V) techniques for space systems, software and algorithms with a focus on specific challenges of space-borne perception and proximity operations uncooperative spacecraft . Develop novel methods
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analytics. The research assistant will develop real-time flight control algorithms, AI- based data processing, and mission management platforms, contributing to the deployment of smart UAV demonstrators under
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, engineering, or a related field. Strong programming skills and experience in machine learning or statistical modelling are essential. Experience with healthcare data, algorithmic fairness, or deep learning
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High-Energy Physics (HEP). We seek highly qualified candidates with interest and experience in ML algorithms including unsupervised techniques, time-series modeling, and clustering algorithms
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, high-performance computing (HPC), and computational sciences. Major Duties/Responsibilities: Participate in: (1) design and implementation of scalable DL algorithms for atomistic materials modeling
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Is the Job related to staff position within a Research Infrastructure? No Offer Description The researcher will use and further develop ptychography algorithms for transmission electron microscopy data
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place technologies and to develop digital twin algorithms to assist clinicians in developing treatment plans for patients with chronic diseases. Analyzes complex sensor data, works with a