21 structures-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL" positions at Nature Careers in France
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role The successful candidate will pursue postdoctoral research in group actions, geometric structures, and smooth dynamics in the research group of Prof. K. Melnick, within the Department of Mathematics
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Interest in contributing to interdisciplinary research Capacity to mentor young researchers and structure emerging research initiatives Leadership and management experience, including the ability
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role Research in the general domain of stochastic analysis, with special focus on stochastic geometry, such as random fields, random graphs and related structures, limit theorems, stochastic calculus and
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sustainability transitions Proficiency in qualitative research methods, e.g. semi-structured interviews, focus groups, stakeholder mapping Strong teamwork skills, scientific curiosity and a high level of intrinsic
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, execution-focused team. Key responsabilities: Lead experimental R&D on advanced material systems for thermal sensing devices Use modelling and structured experimentation to select and converge on top
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aggregation assays is a plus Well-structured and autonomous working style, good organisational and communication skills Team player Early-stage researcher: a researcher without a PhD who has graduated in
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techniques, as confocal microscopes, are widely used in biology for cellular and sub-cellular structures investigation. However, their spatial resolution is limited by the light diffraction phenomena and it is
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techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems
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coordinate large-scale structural projects (shared infrastructure, cross-disciplinary technology programmes, calls for projects). Manage the C2RT: lead its strategic development and oversee the operations
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Keywords: theoretical biophysics, machine learning, kinematics, (structural) biology. Context. Machine learning techniques have made significant progress in prediction of favourable structures from