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, and MS in Computer and Information Science (https://cse.aua.am/ ) invite applications for a full-time faculty position in Machine Learning at the rank of Assistant Professor, starting in July 2026
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research, evidence accumulation modeling, and computational cognitive science. The Luddy School of Informatics, Computing, and Engineering A top-tier program in computer vision, machine learning, and
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Ecole Nationale des Ponts et Chaussées (ENPC) | Champs sur Marne, le de France | France | 3 days ago
-scale (~10’s of km2) permafrost thermo-hydrological hybrid twin, to be coupled with state-of-the-art freezing/thawing soil mechanics machine learning-based surrogate models (Richa et al., 2024, Tristani
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teaching opportunities can be arranged if desired. The ideal candidate should have experience with machine learning, particularly in deep learning or related areas. No prior knowledge of cryptography is
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/10.1016/j.xcrp.2022.101112 and https://doi.org/10.1080/08940886.2022.2114716 key words synchrotron radiation; X-ray Absorption Spectroscopy, machine learning, artificial analysis, autonomous experimentation
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looking for postdoctoral researchers in the area of computer vision, AI, and machine learning. The initial appointment will be for 2 years with a possible extension with a tentative start date in January
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fluids, quantum spin liquids and exciton fluids. • Data Management, Analysis & Machine Learning Knowledge and experience in high volume image-array data acquisition and management, development of custom
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and Engineering / Natural Language Processing Bioinformatics / Science (more...) Computer Science / Learning at Scale Cognitive Science Machine Learning Quantum Machine Learning Artificial
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics