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Center for Drug Evaluation and Research (CDER) | Silver Spring, Maryland | United States | about 16 hours ago
or limited multivariate analysis. However, the increasing dimensionality and complexity of analytical data demand AI/ML-based frameworks capable of learning nonlinear, high-dimensional relationships between
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staff position within a Research Infrastructure? No Offer Description Research activities focus on advancing geospatial analytics and Earth observation (EO) image processing through the development
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/or energy operations. Experience with data acquisition (web scraping and API usage) and data visualization and other analytics tools including machine learning, optimization and simulation. Computer
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computational chemistry, reaction network analysis, and machine learning for organometallic catalytic reactions. 2. Design of membrane-permeable macrocyclic peptide drugs via machine learning structure
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energy storage system or electrochemical system (Batteries) Encourage initiating activities on battery development, devising, and analytic process Experience in machine learning will be preferred
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Generative AI model which will predict the fertility stages of species based on analytical information from blood (or body fluid) samples. The successful candidate will work closely with the project Chief
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-disciplinary areas of artificial intelligence machine learning big data and data analytics software and security mobility and autonomy The Presidential Postdoctoral Fellowship is proudly supported by generous
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This post will advance the application of Machine Learning (ML) in weather forecasting and hydrological prediction. The Research Fellow will develop ML methods for postprocessing numerical ensemble weather
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this. Lets change the world. Responsibilities: Develop novel and biopharmaceutical industry leading peptide, protein, antibody and multispecific antibody MS and MS/MS analytical techniques, automation sample
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, alignment, evaluation). Design multi-MLLM collaboration methods (knowledge transfer/distillation, federated learning). Build efficient training/benchmark pipelines and report results with clear metrics. Apply