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demonstrate exceptional skills and experience relevant to the role. Applications are invited for the post of Postdoctoral Research Associate in Applied Machine Learning for High-Stakes Regulated Domains in
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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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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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and Immigration website . Full-Time, Fixed-Term (12 months) Applications are invited for the post of Postdoctoral Research Associate in Applied Machine Learning for High-Stakes Regulated Domains in
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in machine learning and/or computer security and Experience working with LLMs or agent-based systems. Informal enquiries may be addressed to [email protected] For more information about working at
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Position Title: Postdoctoral Researcher in Machine Learning and Computational Chemistry Appointment Type: Post Doc/Trainee Job Description: Summary of Duties and Responsibilities: The Postdoctoral
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Inria, the French national research institute for the digital sciences | Grenoble, Rhone Alpes | France | 1 day ago
the Thoth project team (https://thoth.inrialpes.fr/ ) within the Inria Centre at Université Grenoble Alpes (https://www.inria.fr/en/inria-center-universite-grenoble-alpes ) to work under the supervision
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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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learning – and we are growing and seeking: Postdoc / PhD Student / Entrepreneurial Postdocs to join the Institute and its wider research community. We welcome applications across all areas of machine
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, extract, and standardise functional information 2. Develop computational tools that integrate evolutionary and functional information using comparative genomics and deep learning approaches 3. Apply