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Field
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spectroscopy Analysis of multimodal spectroscopic data using advanced machine learning techniques Optical design and miniaturization of a novel spectroscopic sensor based on freeform optics, optimized for
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Electrical Engineering is one of the six schools of Aalto University. Our portfolio covers fields from natural sciences to engineering and information sciences. In parallel with basic research, we develop
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 10 hours ago
. Experience developing, training, and evaluating machine-learning models using complex biomedical, imaging, or other high-dimensional datasets. Knowledge of experimental design, statistical analysis, model
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 12 days ago
. Experience developing, training, and evaluating machine-learning models using complex biomedical, imaging, or other high-dimensional datasets. Knowledge of experimental design, statistical analysis, model
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Departmental Website: https://med.stanford.edu/neonatology.html (link is external) How to Submit Application Materials: For inquiries and submitting the required material please contact: anooprao
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5 Sep 2026 Job Information Organisation/Company KTH Royal Institute of Technology Research Field Computer science » Other Engineering » Communication engineering Engineering » Electrical engineering
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, acoustic/electrical characterization, and flow measurements; validate models and identify operating regimes. Develop numerical simulation, data-analysis, and machine-learning/automated rule-extraction
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. Current needs are greatest in power electronics and circuit design. The Electrical Engineering program is part of the Department of Engineering and Mathematics, which is housed in the School of STEM. In
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arrays for neural imaging in bio-machine hybrid systems. At Aalto, our focus is the design and fabrication of photomemristor arrays that meet the demands of interfacing with biological systems, including
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machine learning to antigen and neoantigen discovery, de novo protein design, and the optimization of engineered immune cells. Develop methods for interpreting high-dimensional and perturbation data