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engineering, machine learning, molecular design, and sustainability, helping to create smarter ways of identifying promising sorbents for electrochemical CO2 capture. Over the course of the project
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worldwide and BNL is responsible for data compilation in North America. In order to make full use of the data in EXFOR, experience in data science, machine learning (ML) and applications of artificial
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, computer science, and statistics The objective of this PhD project is to develop machine learning algorithms that perform efficiently and coherently across both classical and quantum computing platforms. The PhD
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, as well as from industry. For more information and how to apply: https://www.jobbnorge.no/en/available-jobs/job/294560/phd-research-fellow-in-deep-learning-for-medical-imaging-and-multi-modal-data-in
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: · A completed M.Sc. degree in computer science, machine learning, and related fields. · Strong proficiency in English (the working language of the institute). · Capability and willingness
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or electrochemical system PhD in Chemistry/Materials Science/Physics Encourage initiating activities on MOF development, devising, and analytical process Experience in machine learning will be preferred Good oral and
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-oriented background - You have a genuine interest in signal processing and machine learning methodology and algorithms - You obtained good grades in courses related to the topics relevant to this PhD
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knowledge and proven capacity of data analytics and machine learning. *Excellent programming in Python, R, SQL. Have experience with tools such as Google analytics, AWS, Looker, Tableau, or similar
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dissemination, and translational opportunities Job Requirements: PhD in Chemistry with a focus on computational/peptide/organic/machine learning or a closely related discipline At least one first-author
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network structures. Methods from graph theory, machine learning, and artificial intelligence will be employed to model complex relational structures and identify patterns in high-dimensional data. The work