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environment. The successful candidate will develop and apply advanced machine learning techniques—including multimodal AI, computer vision, and large language models—to complex scientific and engineering
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
Vision and speech processing, multimodal signals processing and fusion; Proficient programming experience in Python and libraries (e.g., Pytorch, TensorFlow) with several years of practice; First-author
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understand cancer biology, identify diagnostic and prognostic biomarkers, and improve cancer therapy. Projects will involve the development of AI solutions, particularly by leveraging large language and vision
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computer vision methods such as optical flow or motion estimation Experience with geospatial data processing (NetCDF/CF, GeoTIFF, xarray, GDAL/rasterio) Experience with GPU computing or deep learning
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slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and demonstrated experience in computer vision or analysis of pathology images
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/Resume. Required Education and Experience PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field. Preferred Qualifications Strong publication record in top-tier AI
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QUALIFICATIONS: • PhD in Electrical and Computer Engineering, Mechanical Engineering, Physics, or a closely related field. • Demonstrated expertise in MEMS/NEMS design and modeling, including finite element
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. In particular, he/she will be expected to :• Select and evaluate the most suitable approaches from the wide range of machine learning and computer vision methods available in the literature, with
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no more than four years before the start of employment. For well-justified reasons (e.g., parental leave, military or civil service), this limit may be extended. Selection will be based on an overall assessment
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machine learning methods for detecting, classifying, and identifying wireless anomalies in real-world radio environments. You will design and experiment with AI-driven approaches for spectrum analysis, work