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neutrino physics—using a combination of CMB and large-scale structure data. The analysis will rely on modified Boltzmann codes (e.g. CAMB or CLASS) and Monte Carlo techniques for parameter inference
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, economics, geography, psychology or related field working with large data sets. Experience in developing scientific data management tools/processes to manage large volumes of data originating from sensors
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Collaborative, and the Hopkins Business of Health Initiative to manage, analyze, and interpret large healthcare datasets, including Medicaid, Medicare, commercial, and all-payer claims. We are seeking a Sr. Data
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posts) [Appointment period: each for six months] Duties We are building a domain-specific Multimodal Large Language Model (MLLM) devoted exclusively to hepatocellular carcinoma. The appointees will
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Inria, the French national research institute for the digital sciences | Talence, Aquitaine | France | 3 days ago
personalization algorithms. The project will leverage EvidenceB's extensive deployment infrastructure to work with authentic large-scale educational data and validate innovations in real classroom settings. In
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Fixed Term: Until 31 August 2029 Full Time: 35 hours per week The Opportunity: A Postdoctoral Researcher Position in Data Science/Molecular Epidemiology is available within the Marioni Group
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Classification Title: Large Animal Hospital Client Liaison I or II- (CARE) Classification Minimum Requirements Technician I - High school diploma or equivalent and two years of experience loading
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), alongside experience working with large-scale biological, clinical or imaging datasets and experience building or maintaining data pipelines (e.g. ETL processes). You will be proficient in Python and relevant
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of points within a short time, yielding large amounts of data that need to be visualized and analysed instantaneously during a beam time. Additionally, the varied possibilities at the beamline generate
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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
modeling of large-scale dynamics in networks. This role involves creating large scale models of dynamic phenomena in electrical power networks and quantifying the risk of rare events to mitigate