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strands. • Bespoke modelling of tumour metabolic function using 3D and 4D imaging data • Cancer patient risk prediction using machine learning (with experience in particular in radiomics and transcriptomics
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SPECIFICS The Department of Learning and Performance Systems in the College of Education at Penn State invites applications for a Postdoctoral Scholar in AI and the Learning Sciences. This is a full-time, one
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cross-spectra, auto-spectra, matched filtering, and related methods. Demonstrated ability to work independently and as part of a group. Knowledge, Skills & Abilities: Knowledge of underwater active
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Intelligence and Machine Learning. Join a team of scientists at the leading macromolecular crystallography beamlines and at the computing center and contribute to science projects at the interface between AI
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in AI for genomics (e.g., generative models, transformers, genomic language models, agentic AI) and related areas of statistics (e.g., uncertainty quantification for machine learning and AI). Apply
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paired with computational biology and machine learning to develop predictive AI models of how cells interpret and respond to the surrounding extracellular matrix. Required Qualifications: We are looking
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converter degradation/failure analysis. Brief Description of Duties: The Postdoctoral Associate will support the Principal Investigator, Dr. Fang Luo , in the Department of Electrical and Computer Engineering
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converter degradation/failure analysis. Brief Description of Duties: The Postdoctoral Associate will support the Principal Investigator, Dr. Fang Luo , in the Department of Electrical and Computer Engineering
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quantum chemical methods and machine learning; developing quantum algorithms for computational chemistry on quantum computers; and applying existing and new computational methods to study multiscale
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 3 months ago
of novel computational approaches at the intersection of semiempirical quantum chemistry, machine learning potentials, and implicit solvation models. Requirements Experience in computational chemistry