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diversification and change we see across languages today. Job description This 4-year PhD project will use group communication games and artificial language learning experiments to investigate the role of social
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precision medicine based on gene sequencing time series data. Large data sets come with significant computational challenges. Tremendous algorithmic progress has been made in machine learning and related
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for part-time employment. Starting date: 27.03.2026 Job description:PhD position on physics-based machine learning modeling for materials and process design Reference code: 2026/WD 1 Commencement date
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PhD Position - Marie Curie network ON-Tract: Protein engineering of enzymes: in vitro directed evolution and machine learning-based elaboration of biocatalysis for synthesis. A doctoral position is
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desirable) Interest or experience in Earth system modelling, remote sensing, and/or machine learning Ability to work independently and collaboratively in an interdisciplinary environment High proficiency in
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: machine/deep learning, numerical modelling, statistics, optimisation, scientific computing • Ability to work across disciplines and collaborate with academic and industrial teams Desirable: • Experience in
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evasion mechanisms of pathogens. This project directly aims at tackling this question and will be done in collaboration with the Link laboratory. The core of this work will be to screen bacterial libraires
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. The candidate will pursue research in machine learning, numerical optimisation, and process systems engineering, and collaborate with academics from the Department, the Sargent Centre, and industrial
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. At the Division of Systems and Control , we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and
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users, thanks to the use of machine learning tools and techno-economic analyses. This project is aligned with the sustainable development goals (SDG) 7 and 10 of the United Nations, by promoting a low