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Field
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to the discussions with the project partners (with reciprocal visits during the employment period) and work towards the project deliverables; explore new methods construct LRC codes from algebraic curves and/or
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along with complementary structural biology methods. The successful fellow will work at the Institut Laue Langevin (ILL) to develop and apply cutting-edge neutron crystallography approaches
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be working primarily with scientific machine learning methods, including symbolic regression and neural networks. You will apply the algorithms to the discovery of new models in different fields
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SGS modeling) and RANS. This includes proposals of new methodologies, implementation and validation of the methods using the simulation and experimental data, reporting of the results, and dissemination
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impact. Your key responsibilities will be to: conduct high-quality, original research on AI-enabled decision-making dynamics in complex, large-scale project systems develop formal, computational
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algorithmic perspectives on large language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems
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EXPERIENCE in: Scientific/Academic writing Research methodology (Quant or Mixed Methods) Experience with statistical software (e.g., R, Mplus, SPSS, etc.) Credentials must be obtained prior to the start date
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the life sciences” as outlined in the project description. Applicants to the PhD fellowship must hold a two-year Master’s degree (or equivalent qualifications) and meet the formal requirements for admittance
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language models Statistical learning theory and complexity analysis Automated theorem proving and formal methods Random matrix theory and its applications in modern AI systems This position can be filled
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) Willingness to work in a highly interdisciplinary context and develop a set of interdisciplinary methods (i.e., dealing with both qualitative and quantitative data) Interest in engaging with stakeholders active