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CW and pulsed laser systems, spectrometers, high-resolution cameras, and delicate optical components are desirable Expertise in advanced data analysis techniques (Machine learning and Deep learning
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technologies like Azure Data Explorer (ADX), PostgreSQL, MongoDB, and data driven machine learning tools such as Spark, Power BI. Experience with optimisation, simulation and data analysis tools such as Matlab
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning
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should have experience in real-time processing or FPGA-based prototyping or embedded sensing architectures, or machine-learning-driven analysis for photon-limited measurements. Exposure to event-driven
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, and evaluation approaches. 2) Observe and learn how longitudinal studies using large administrative health claims and other public health datasets are designed, conducted, and interpreted. 3) Gain
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the following training will be considered PhD in computer science, machine learning, AI or related computational field, or, Ph.D. in a health-related discipline with experience in experimental science, devices
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interested in applicants who use advanced quantitative methods, including computational modeling, machine learning, and/or analyzing structural and functional neuroimaging data. Specific activities may include