20 distributed-algorithm-"Meta"-"Meta"-"Meta" research jobs at Pennsylvania State University
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engine. Algorithm Development: Contribute to designing, prototyping, and testing algorithmic models for personalized content recommendations. System Evaluation: Support the evaluation of the recommendation
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Scientific Machine Learning. The successful candidate will develop and deploy state-of-the-art SciML algorithms in high-performance computational physics codes. We accept applications from all candidates with
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research involving biological data analysis and modeling of biological systems. In particular, they will develop and apply algorithms to construct discrete dynamic models of signal transduction networks
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experience with data annotation and labelling, as well as understanding of (coursework) in machine learning algorithms Schedule: up to 20 hours per week Compensation: The starting rate for this job is $15
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occupant harm from exposure to indoor bioaerosols. Key Responsibilities Responsibilities include, but are not limited to: Developing and analyzing new HVAC control algorithms to balance energy efficiency and
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REQUIREMENTS: The Department of Astronomy and Astrophysics seeks to hire an undergraduate student or recent graduate to work part-time as a Research Assistant. Typical Duties: - Develop Python algorithms
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REQUIREMENTS: List of Responsibilities/Job duties: Research focus: Socio-technical and legal impacts of datafication and AI on communities, especially worker groups and including black-box algorithms and large
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on Machine Learning and LLMs. Read and analyze research papers to design novel AI solutions. Develop and fine-tune LLMs to enhance AI-driven educational tools. Implement machine learning algorithms and
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of the following: quantum field theory, quantum information, out-of-equilibrium dynamics, theoretical cosmology, particle physics, gravity, or quantum algorithms. Candidates are expected to demonstrate
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Sriperumbudur. Potential research projects include (but are not limited to) developing theory and methods for metric-valued (including functions, distributions) data analysis, optimal transport and gradient flows