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Field
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The Department of Ecoscience at Aarhus University invites applications for two postdoctoral positions to strengthen our research on image recognition, computer vision and deep learning applied
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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neuroscience, and brain-computer interfaces, machine learning and deep learning, statistical modelling, regression methods, and uncertainty quantification, calibration, interlaboratory comparisons, and
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Post-Doctoral Associate in the Center for Interdisciplinary Data Science and Artificial Intelligence
); Relevant research experience in at least one of the following: Social Robotics, human-robot interaction, human-computer interaction, Machine Learning, Affective Computing, Social Signal Processing, Computer
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contribute to high-impact research on machine learning related to the analysis of IoT data in the form of multivariate time series. The DESS group is part of Department of Computer Science that belongs
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generation, media forensics, anomaly detection, multimodal learning with an emphasis on vision-language models, computer vision applications for space. Key responsabilities: Shape research directions and
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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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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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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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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 2 hours ago
engineering or related fields. The ideal candidates will be rising sophomores or juniors with experience using Python or Artificial Intelligence (including Robotics, Computer Vision, Human Language Processing