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; develop and validate an astrodynamics-based orbit determination algorithm using TFC, including hybrid solutions with stochastic filters (eg, EKF or UKF); integrate and calibrate optical sensors and develop
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(NLP) algorithms applied to electronic health records (EHR) to understand cannabis-related harms in aging PWH and people without HIV. The position will entail collaborations with several investigators
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Intelligence (AI) algorithms, including Machine Learning (ML) and Deep Learning (DL) techniques, for advanced signal analysis. The work will focus on developing methodologies for the detection, extraction
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languages like Python or C, and or developing and/or using computational methods for analyzing large datasets. Demonstrated experience in developing computational algorithms for solving problems, preferably
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patients from public repositories (including dbGaP). Develop and apply machine learning algorithms to associate patterns in the data with cancer progression and therapeutic response in prostate cancer
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, TensorFlow, or JAX) Experience with photonic design techniques and global optimisation algorithms (Genetic Algorithms, Particle Swarm, Gradient Descent) Experience implementing deep learning architectures
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
world. Position Summary The postdoctoral researcher will conduct advanced research in artificial intelligence (AI) and machine learning, with a focus on developing novel algorithms and systems. The position offers
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Using Machine Learning Algorithms (Plasmon-2Detect), ref. COMPETE2030-FEDER-00714300, number of the project - 16004, financed by the European Regional Development Fund (ERDF), with a view to the
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(http://vanallenlab.dana-farber.org/) to work on the analysis of new datasets generated in the context of multiple clinically oriented cancer sequencing projects in order help advance efforts
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will contribute to high-impact projects, including: 1. Developing and validating algorithms that extract data from the Epic EHR (e.g., large language models) via comparison with manually extracted data