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(payable 14 times per year) Responsibilities The applicant is expected to establish an own research group with focus on advanced machine learning and deep learning techniques for remote sensing applications
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 10 days ago
-series. Experience exploring machine learning and deep learning techniques for geospatial applications is highly desirable to effectively engage with Earth observation foundation models. Technical
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. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning
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-of-the-art bioinformatics approaches, increasingly incorporating AI and deep learning (see, e.g., Sarropoulos et al., Science 2026). This work has provided insights into the origins and functional evolution
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earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc. Documented experience in machine learning, in particular deep generative
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working experiences, and professional mentorship and support. The postdoctoral associate will be affiliated with the Center for Avian Population Studies (CAPS, https://www.birds.cornell.edu/home/center
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includes the opportunity for three weeks of training in higher education teaching and learning. The postdoctoral fellow will: Develop and maintain harmonized satellite time-series datasets (Landsat and
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. Education and scholarly development The postdoctoral associate will receive structured education in computer vision applications in medical imaging, machine learning, research methodology, responsible conduct
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modelling (e.g., LSTMs) and deep generative/unsupervised anomaly detection techniques (e.g., VAEs). *Strong programming proficiency in Python and familiarity with standard data science and machine learning
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theoretical advances into practical insight and tools that can support analysis, design and decision-making for AI-enabled space systems, thereby bridging the emerging scientific theory of deep learning with