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that combine principled reasoning with the efficiency of modern machine learning to enable intelligent, real-time decision-making in large-scale interconnected systems. This position offers the opportunity
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-based control and decision-making for complex multi-agent systems. The project explores new computational frameworks that combine principled reasoning with the efficiency of modern machine learning
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of visualization and multimodal machine learning. Admission requirements The general admission requirements for doctoral studies are a second- cycle level degree, or completed course requirements of at least 240
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Grant, focusing on the development of novel deep learning tools to recommend reaction conditions for the synthesis of novel TRPA1 inhibitors. The project “A machine learning approach to computer assisted
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Classification Title: Post Doc Associate Classification Minimum Requirements: A Ph.D. in biomedical informatics, computer science or a related field. Working experience with large language models
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for the position. Preferred selection criteria Scientific publications are an advantage Experience in research project works Good knowledge and experience in the use and development of machine learning algorithms
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Post Doc Associate, PSiX The Purpose Science and Innovation Exchange (PSIX) at Cornell University is seeking a full-time Postdoctoral Associate to play a key role in advancing the PSiX initiatives
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. The candidate will work closely with the CIs, post doc and PhD (machine learning) candidate, to develop choreographic structures used to generate movement and interaction capabilities that will define human-robot
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) with excellent grades in computer science, materials science, physics, or a related discipline Practical experience in data science, including the application of machine learning (ML) methods or large
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 1 month ago
. Preferred qualifications: D. in Quantitative Genetics/Genomics, Computational Biology, or Related Discipline. Skilled in single-cell transcriptomic analyses, machine learning and artificial intelligence