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This position focuses on investigating vehicle-track-ground interaction dynamics with a particular emphasis on the critical speed induced by high-speed trains. The candidate will contribute
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presentation of analysis results. The ability to work with large and complex datasets. Excellent spoken and written English skills. Experience in machine learning, predictive modeling, and/or Bayesian methods
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fungal communities may be changing through time, and infer the functional significance of this, in terms of changes in organic matter composition, and C accumulation rates. About the position The postdoc
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analysis, work with large language models, network analysis, causal inference in machine learning and agent-based modelling. Experience in collecting, curating and analyzing large digital datasets with
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Network Inference https://www.umu.se/en/ucmr/ec-postdoc-programme/ncrna_net-development-of-a-novel-approach-to-lncrna-mrna-regulatory-network-inference/ 7. Virome–vector competence shifts: How mosquito
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analysis, work with large language models, network analysis, causal inference in machine learning and agent-based modelling. Experience in collecting, curating and analyzing large digital datasets with
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. Teamwork experiences should also be documented in the application. Since the subject is male-dominated, we encourage female applicants. Proven track record of publication will be preferable. Applicants will
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: Holds a relevant doctoral degree with a solid track record. Technical Proficiency: Expertise in bioengineering, additive manufacturing or stimuli repsonsive materials is a plus. Self-Motivated: Strongly
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agent behavior Strong collaborative skills and a track record of publications Preferred qualifications A doctoral degree or an equivalent foreign degree, obtained within the last three years prior
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/containerization platforms (e.g., Kubernetes, OpenStack) Ability and motivation to carry out high-quality research and development Strong track record of publications in leading Q1 journals and top-tier