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: January 19, 2026 (for full consideration) Apply online at AJO: https://academicjobsonline.org/ajo/jobs/31255 The Center for Data Science for Enterprise & Society at Cornell University seeks to recruit
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the project’s deliverables. 2. To perform internationally competitive research in the analysis of transcriptomic and epigenetic data. 3. To provide analytical and statistical expertise, and algorithms for data
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a PhD student, you will develop state-of-the-art learning and inference methods to detect and characterize anomalous radio behavior and to design algorithms that remain reliable under practical
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requirements: Experience using deep-learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating on scientific projects. Publications on deep-learning topics. 4. Work Plan
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be duly proven at the time of hiring. 2; 3. Preferred requirements: Experience using Machine Learning algorithms. In-depth knowledge of Python and PyTorch. Previous experience collaborating
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signal processing and machine learning algorithms, have strong interpersonal skills and the ability to work in an international team. Experience in wireless communication and networking fundamentals is a
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, and inline interpretation. The work includes validating algorithms on a lab-scale sorting setup and linking spectroscopic outputs to recyclability scoring and Digital Product Passport data flows. Your
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datasets Implement LLM and Machine Learning algorithm Conduct statistical analysis in SAS or Stata Assistant with other ad hoc tasks Required Education Bachelor’s or Master’s degree in computer science
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 2 months ago
Lidar and the Roscoe upper troposphere/lower stratosphere lidar). Additional projects include the development of machine learning and advanced data processing algorithms, and participation in upcoming
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on the complementary expertise and recognized excellence of its 22 research teams to contribute to the development of the fundamental aspects of computer science (models, languages, methods, algorithms) and to foster