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knowledge of process systems engineering. The position aims to advance physically consistent and predictive thermodynamic modeling, including the integration of advanced machine learning methods, to support
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processes, targeting annual savings of £280,000. Responsibilities include creating and refining models to predict particle behaviour, calibrating them to 95% accuracy, and establishing sensor systems for real
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that contribute to the control of viral infectious diseases. The lab’s research focuses on two main areas: (A) developing AI technologies to predict genotype–phenotype relationships of viral proteins, and (B
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mechanics and foundation engineering in light of geologic conditions, recognition, prediction, and control or abatement of subsidence, landslides, earthquakes, and surface/subsurface disposal of liquids and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 3 hours ago
Associated buildings. Responsibilities include but are not limited to operating and performing preventive/predictive/reactive maintenance and repairs on commercial, industrial and residential HVAC DX
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of £280,000. Responsibilities include creating and refining models to predict particle behaviour, calibrating them to 95% accuracy, and establishing sensor systems for real-time data acquisition. You will
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processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job description The Department of Decision and Control Systems is looking for up to two research engineers
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18 schools and central DAR staff to identify and engage Penn’s most impactful prospects. This position emphasizes a full-stack development philosophy, utilizing GitHub for version control and peer
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processing, quality control, integration, and analysis of single‑cell and multimodal omics datasets (e.g. scRNA‑seq, scATAC‑seq). Train, evaluate, and benchmark deep learning models operating on single‑cell