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in Computer Science, Machine Learning, Artificial Intelligence, Computational Biology, or a closely related field Has strong theoretical and practical experience in deep learning Has hands
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Profile: A Master`s degree and an excellent PhD degree in Biochemistry, Chemistry, or a related Molecular Science Proven Track Record in Machine Learning, Molecular Simulations, Chemoinformatics
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | 21 days ago
acquisition Good communication skills in English and ability to collaborate in interdisciplinary teams Desirable qualifications Experience with machine learning methods for regression or signal interpretation
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
solid academic background in polymer synthesis and electrochemistry. The candidates should be self-motivated to explore new areas of studies including AI related and machine learning fields, have evidence
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year. You should have knowledge and experience in bridging quantum and classical machine learning, and be fluent in English, both written and spoken. Assesment criteria Qualifications that are considered
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. Requirements: PhD completed less than 7 years ago in Computer Science or related areas; experience in machine learning and data science (supervised/unsupervised models, recommendation and evaluation/robustness
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Electromagnetic simulation We also welcome the researcher from different fields: AI (machine learning, big database, etc) Semiconductors As a minimum requirement, you must have a PhD degree in Electrical and
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of Engineering, located in Brooklyn, NY, is deeply committed to excellence in teaching and learning. Tandon fosters student and faculty innovation and entrepreneurship that make a difference in the
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the HNSCC team, including Taran Gujral (machine learning-enabled drug screening), Slobodan Beronja (mouse models of HNSCC), and Patrick Paddison (functional genomics). This work will encompass a broad array