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Field
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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 systems
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AI Algorithm and Autonomous Decision-Making Systems RESPONSIBILITIES Research and develop autonomous decision-making algorithms for process industries (e.g., chemical, pharmaceutical, materials, energy
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Computer Science, Chemistry, Chemical Engineering, Physics, or Materials Science. You will develop optimisation and machine-learning algorithms for human- and literature-informed discovery of new materials
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Diabetology (DIAB) invites applications for 1 Postdoc (m/f/d) starting as soon as possible. We are seeking a highly motivated Computational Biologist/Bioinformatician to join our team which focuses
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Computer Science or a related field, with a focus in databases, data systems, theory, or algorithms. Strong publication record in top-tier venues. Solid background in one or more of the following: Query processing
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regional leadership in biostatistics, genomics, biomedical informatics, artificial intelligence and health data science. The Postdoctoral Associate will conduct research in statistical machine learning and
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The College of Engineering and Computer Science at Texas A&M University–Corpus Christi (TAMU-CC) invites applications for a Postdoctoral Research Associate to advance the state of the art in safe, efficient
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analyzing current or future quantum simulations at the intersection of subatomic physics and quantum information science. The successful candidate will also lead peer-reviewed publications and develop
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
The Mathematics and Computer Science (MCS) Division at Argonne National Laboratory invites outstanding candidates to apply for a postdoctoral position in the area of uncertainty quantification and
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into operational decision‑support tools for farmers, in close collaboration with an industry partner. The project focuses on automated rumen‑fill assessment using 3D imaging, computer vision, and predictive