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Field
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. Strong applied knowledge of classical machine learning algorithms, including implementation with scikit-learn, model evaluation using metrics such as accuracy, precision, recall, F1-score, and
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interaction physics, precision calculations of (g-2) and PDF determinations, and the development of new algorithms, among others. Position Description The successful candidate will be expected to conduct
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writing C++ and PyTorch. Training and debugging RL agents. Imitation Learning algorithms for robotics or autonomous vehicles. Prior work combining RL with human data or feedback. A track record of code
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implementing new informatics tools and resources to enhance phenotyping performance or enable deep phenotyping through terminology/ontology, natural language processing, and machine learning. The role involves
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Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Ph.D. (or foreign equivalent) in Electrical Engineering, Computer Science, Applied Mathematics, or a closely
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systems biology, bioinformatics, algorithm development, and leading-edge technologies including single-cell sequencing. Benefits (Exempt Regular): UMB offers a comprehensive benefits package that
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a motivated individual to support research and development tasks relating to algorithm development and data analytics requiring a background in the geosciences and data sciences. This includes tasks
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to apply. Responsibilities include: Algorithm development and/or software design Develop C/C++ or equivalent code to interface with developed hardware and associated interfaces (SPI, I2C, etc) Integration
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develop technical solutions. Direct lower-level staff by reviewing their completed work. Develop/change data input, files/database structures, data transformation, algorithms, and data output by using
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qualified candidates to apply for a postdoctoral research position. The research will focus on statistical modeling, machine learning and trustworthy AI to develop effective and efficient algorithms