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About the project: Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy Supervisor: Dr Sara Sangtarash, University of Warwick Thermoelectric materials convert heat
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research. Experience applying machine learning and statistical modeling techniques to large biological datasets for biomarker discovery, disease prediction, or host-pathogen investigations. Proficiency in
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» Cybernetics Engineering » Control engineering Engineering » Mechanical engineering Engineering » Electrical engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application
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Your responsibilities: As a University assistant, you will contribute to the work group Machine Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods
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bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 30.04.2029 Reference no.: 5311 Your responsibilities: As a University assistant, you will contribute to the work group Machine Learning
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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, materials science, and physics. Supported by 19 countries, the ESRF is an equal opportunity employer and encourages diversity. Context & Job description Thesis subject: Machine Learning for Neutron
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technologies (such as electric vehicles and heat pumps) in Low Voltage (LV) networks. PEDs include Soft Open Points, Static Compensators (STATCOMs), and Smart Transformers (STs), which have been trialled and
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their own vehicle and find their own housing for the duration of the position. QUALIFICATIONS Knowledge of computer programming language(s) for data analysis and visualization Knowledge of spreadsheet
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application! We are looking for a PhD student in Statistics and Machine Learning Your work assignments We are looking for a PhD candidate to work in the intersection of computational statistics and machine