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PhD Studentship: Green vs. Non-Green Innovation: The Role of Regulatory Policies and Market Dynamics
equivalent) is required. Candidates from disciplines such as mathematics, statistics, computer science, economics, or related quantitative fields are strongly encouraged to apply. While applicants need not be
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Modelling, Applied Statistics, Linguistics, Data Analysis, Large Language Models, Machine Learning. Start date: 1st October 2026 Deadline: 30th April Duration: 36 months Funding: Funded Funding towards
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Max Planck Institute for Dynamics and Self-Organization, Göttingen | Gottingen, Niedersachsen | Germany | 27 days ago
fundamental physical mechanisms using computer simulations and the methods of non-equilibrium statistical physics. The Research The PhD student will work under the supervision of Dr. Philip Bittihn within
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, signal in-tegrity) under varying physiological and environmental conditions Conduct statistical evaluation and model validation using controlled measurements and sensing-dummy reference data Collaborate
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and drug–excipient interactions that determine formulation stability and performance. The research will employ atomistic molecular modeling grounded in statistical mechanics to investigate binding
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for participants. Given the practical and societal relevance of the project, this studentship is well suited to applicants who are motivated by research with real-world impact, but with strong statistical and
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include teaching or other departmental duties, up to a maximum of 20 per cent of full-time. Your qualifications You have gratuated at Master’s level in machine learning, computer science, statistics, or a
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subject. Practical experience of applying specialist skills and techniques required for the role including human interventions, polyphenol chemistry, databases and statistics. A proven track record of using
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of electrical engineering are an asset. Candidates should be able to prepare and conduct greenhouse experiments with tree seedlings and independently conduct statistical analyses with R. Candidates
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two main parts: • numerical modeling of crustal stresses associated with the various proposed mechanisms; • statistical analysis of the impact of these stresses on faults, potentially active or not, and