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optical systems and the use of lasers. Proficiency in data processing software e.g., Python, Matlab. Exhibits excellent professionalism and work ethics, initiative and self-motivation. Strong ability
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Documented knowledge of statistical theory and methods and core machine learning methods Documented Python programming skills Desired qualifications: Experience with change point detection and anomaly
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: Experience with one or more general-purpose programming languages, such as Python, and general-purpose deep learning frameworks. Experience with integrating multidisciplinary inputs. Demonstrated ability in
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Center for Devices and Radiological Health (CDRH) | Southern Md Facility, Maryland | United States | about 22 hours ago
on the development of optical models, image acquisition software, image processing algorithms, and deep learning algorithms using Zemax, Python, Matlab, LabVIEW, CUDA, and other programming environments. Training
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experience in scientific programing (using e.g., Python, Matlab, and/or Julia), as well as a strong interest in climate science and biogeochemical cycling. The candidate must have proof of excellence in
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) corresponding to a minimum of four years in the Norwegian educational system Have programming experience in Python, specifically for scientific data processing and analysis Fluent oral and written communication
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., Python, MATLAB, R, or Julia) A strong interest in land-atmosphere interactions, data assimilation methods, and land-surface modelling is required. The ability to meet the requirement for security clearance
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(or equivalent) for both the programme as a whole and the Master's thesis. Strong programming skills (Python preferred) and foundations in machine learning. Interest in adaptive interactive systems, biofeedback
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mathematical and quantitative skills • Familiarity or experience with data analysis methods (e.g. use of Stata, R, Python, or Stata) • Ability to communicate complex information clearly • Excellent ability
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with healthcare data (e.g., EHR, clinical text, imaging, omics). Proficiency in Python and ML tooling (e.g., PyTorch, scikit-learn), version control (Git), and experiment tracking (e.g., Weights & Biases