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assimilation, machine learning, and optimization techniques. Experience in student mentoring. Publications in leading journals within the field. Preferred Qualifications PhD in Environmental Modeling. More than
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research staff, PhD students, or postdocs Providing guidance, training, and technical support to others in the research team Ensuring compliance with research ethics, safety regulations, and institutional
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at https://puwebp.princeton.edu/AcadHire/position/40281 and submit a current curriculum vitae, research statement, and a cover letter. Contact information for three references is required. To learn more
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processing, machine learning, statistics or related fields. Demonstrated expertise in ML/AI, with prior experience of applications in the healthcare domain, particularly in cancer research considered a strong
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to detail. Proactive and self-motivated mindset with an eagerness to learn and grow Excellent computer skills with demonstrated proficiency in Microsoft Office Suite. Please include a cover letter detailing
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develop methods to disentangle dynamic, multiscale ecological signals from large, heterogenous observational data. This work lies at the interface of statistics, machine learning/AI, ecology, and
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or similar. Experience in handling dynamic modelling and control, experimental setup and testing, Digital Twin and Machine Learning Publication experience Collaboration and/or management skills Communication
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computational modeling to identify bacterial strains and metabolites that promote or hinder probiotic establishment. By combining multi-omics data with systems biology and machine learning approaches
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the last three years prior to the application deadline. Experience in some of the following areas is meritorious: AI and machine learning; convex analysis; functional analysis; mathematical statistics
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Beginning Winter semester Application deadline All students – online application: 1 March for the following winter semester https://www.lmu.de/psy/de/studium/doctoral-training-program-in-the-learning-sciences