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collaborators. Requirements: Applicants should have a PhD in Computer Engineering, Computer Science, or a related field. Extensive and sound knowledge of ML, AI, DNN, LLMs/VLMs, Multimodal LLMs, RAG, Agentic
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cheminformatics or polymer informatics, molecular representations, descriptor engineering, RDKit, characterization-informed modeling, multimodal data fusion, interpretable machine learning, NLP, text mining
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CeMM - Research Center for Molecular Medicine of the Austrian Academy of Sciences | Austria | 2 months ago
CRISPR screening (Bock 2022 Nature Reviews ), and CAR T cell engineering (... Where to apply Website https://www.eurosciencejobs.com/job_display/293955/CeMM_International_Postdoc_P… Requirements Additional
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National Energy Technology Laboratory (NETL) | Morgantown, West Virginia | United States | about 1 month ago
: Experience artificial intelligence, machine learning, digital twin technology, control systems, or computer simulation Point of Contact Cinnamon Eligibility Requirements Degree: Master's Degree or Doctoral
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together computer scientists, AI researchers, domain scientists, software engineers, and high-performance computing experts. You will help design and implement new methods for multimodal federated learning
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Welcome to Maastricht University! You just finished your PhD trajectory and looking for the next step in your academic career? Your interests lie in the field of machine learning techniques
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initiatives, and establish standards to advance machine learning ( OpenML.org ) OpenML is a popular open science platform for sharing interconnected AI artifacts (e.g., datasets, models, and benchmarks) using
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U.S. Department of Energy (DOE) | Washington, District of Columbia | United States | about 9 hours ago
Graphics Design (1 ) Computer, Information, and Data Sciences (17 ) Earth and Geosciences (21 ) Engineering (29 ) Environmental and Marine Sciences (3 ) Life Health and Medical Sciences (6
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data or large data volumes in all information systems. We contribute methods and algorithms for machine learning, and data mining, including XAI, as well as for data access and query processing. Aarhus
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models for the fire resistance of LSF walls, including the development of machine learning models, experimental testing, and numerical simulations, within the project “FireLSF – Development of Predictive