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Field
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Qualifications This position requires a PhD in Computer Science, Electrical Engineering, Artificial Intelligence, Biomedical Imaging, Computational Biology, or a closely related field. Research Record: A strong
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community. We are particularly interested in candidates with: Strong computational and bioinformatics expertise combined with interest in cancer biology and gene regulation. Background can be PhD or even MSc
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Qualifications:. PhD in one of these fields: Computer Science, Data Science, Biomedical Informatics, Computational Biology, Information Science or a related field. Experience in at least one of the following
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developing a screening tool to support clinical evaluation for psychosis risk, powered by generative artificial intelligence. You will lead a project to develop a novel research method for identifying and
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(R1) Positions PhD Positions Application Deadline 4 Oct 2026 - 00:00 (UTC) Country France Type of Contract Temporary Job Status Full-time Offer Starting Date 4 Jan 2027 Is the job funded through the EU
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staff position within a Research Infrastructure? No Offer Description The Institute of Biomedical Engineering conducts research at the intersection of medical wearables, artificial intelligence, and
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: Computer Science or Computer Engineering; • PhD: Computer Science or related fields within the last 7 years; • Knowledge of: Python, DL, CV, Transformers, LLMs, NLP; • Relevant scientific publications; • English
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National Energy Technology Laboratory (NETL) | Pittsburgh, Pennsylvania | United States | about 24 hours ago
of Yuhua Duan. This project will be hosted at the NETL Pittsburgh, PA campus. Although material modeling with artificial Intelligence/machine learning (AI/ML) applications and experimental instrumental
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the crossover between artificial intelligence and Archaeological Prospection. Archaeological prospection faces an unusual combination of challenges: data are sparse and unevenly distributed, observations
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–experiment (ModEx) approach accelerated by artificial intelligence (AI) to advance predictive understanding of how plant–microbial–soil interactions vary across inundation and salinity gradients to shape