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, Organometallic, Organic Chemistry and Machine Learning for a period of up to 24 months. The project, funded by EPSRC, will involve exploring the use of machine learning to develop new tools for investigating
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for entry into a PhD program. A background in machine learning, inverse problems, scientific computing, or related data-driven methods is highly desirable. You are curious about combining physical modeling
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Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. | Dortmund, Nordrhein Westfalen | Germany | about 13 hours ago
. or Diploma in bioinformatics or a comparable qualification Extensive programming experience Practical experience in machine learning and the application of large language models Knowledge of OMICS and image
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and sustainability; Investigate and apply artificial intelligence and machine learning techniques, including large language models (LLMs), across CENSE’s scientific body in its five thematic areas
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is seeking a highly motivated PhD candidate to work on a fundamental research project on systems and control theory for learning in neuromorphic circuits. Neuromorphic computing is an analog, brain
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in epidemiology, causal inference, genetic epidemiology, and machine learning. As a PhD candidate in the project, you will: Actively participate in group meetings, design statistical analysis plans in
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PostdoctoralResearcher in Radio Frequency Fingerprinting using Machine Learning who has: strong experience in signal processing, machine learning or a related field a PhD (or near completion) in signal processing, machine
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applications for a PhD Research Fellow position in fairness in artificial intelligence, available at the Department of Informatics, in the Scientific Computing and Machine Learning (SCML) research group
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Eligibility criteria Selection will be based on the following scientific and technical criteria: • PhD in computational biology, machine learning, bioinformatics or a related field. • Proficiency with Python
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Join us in developing machine-learning accelerated simulation methods to understand and optimize interfaces in hybrid organic-inorganic materials for sustainable energy devices. Your work