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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 12 days ago
, and medicine. Key Responsibilities Collaborate with researchers to design, develop, and refine large language and generative models. Develop novel algorithms for generative modeling tasks and optimize
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, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a travel allowance and access to advanced computing resources. The MMD group is responsible
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optimization under uncertainty, constrained query evaluation, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms
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algorithms that approach these limits. The project uses neural networks to design receivers for the nonlinear optical fiber channel. The project is funded by an ERC Starting Grant from the European Research
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uncertainty quantification. The position comes with a travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and
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algorithms where the agent can propose updates to its own world model structure, but these updates are only accepted after a formal verification step confirms that the new model still adheres to its core
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typologically diverse languages Creating self-supervised learning algorithms that can assess phonological development and speech complexity in children from birth through age 6, with applications to both typical
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quantum information, develop techniques for quantum control and measurement, build quantum computing hardware and software, and explore novel applications. Our main interest is to propose quantum algorithms
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Postdoctoral position in Bioinformatics/Computational Biology (m/f/d) (full-time position 100 % ~ 38
package development) and command line-based analysis tools (e.g. Python) • Knowledge with public sequence databases, error correction algorithms • Scientific experience in immunology, molecular and cell
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space)? What are appropriate descriptors of spatial distribution in the field of materials science (e.g., Voronoi tessellations, particle-particle distances, etc.)? What are appropriate algorithms