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-including evolutionary algorithms, ant colony optimisation, and simulated annealing-to fine-tune an LLM/agent that generates high-quality prompts, inputs, and tool-use strategies for density functional theory
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Schedule: Monday – Friday, 8 a.m. – 5 p.m. Summary The Cheng Lab in the Department of Medicine, Section of Epidemiology & Population Sciences is searching for highly motivated and talented post-doc
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for: • Contributing to various tasks related to the modeling of lipids and membrane proteins involved in lipid droplet biogenesis. • Developing and implementing the POP-MD algorithm in the OpenMM software
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this short-term project we shall use active learning to accelerate the training of deep learning algorithms for optimising 2D material van der Waals (vdW) structure discovery. The goal is to make model
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in clinical trials. Regularly report on improvements or challenges. Documentation will occur via an online database 4. Support training and testing sessions: Coordinate the scheduling of training and
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, design, implement, and validate an optimization system for scheduling group classes within the Koachy platform. The specific goals include: Develop attendance prediction models for different types
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 2 hours ago
. around Mars) through a set of cooperating communications relays, the burden on DSN scheduling can be significantly reduced. The use of frequency division (OMSPA) enables all probes communicating through
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. Location of Vacancy Part/Full Time Full Time Hours per Week 37.5 Work Schedule Monday – Friday 8:30 AM to 5:00 PM Must be willing to work a flexible schedule to meet the needs of the department. Type
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-edge research in machine learning and automated reasoning for safe algorithmic systems. The Research Fellow will be responsible for developing advanced theory and machine learning algorithms
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algorithmic performance. For instance, the scheduling problems that an electric grid operator faces will change daily, but not drastically: although demand will vary, the network structure will remain largely