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The successful candidate will develop generative machine-learning methods for amorphous molecular thin films — the supramolecular structures that govern the performance of organic-electronic materials
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combination with data science approaches such as machine learning and data assimilation via cryospheric models. A main focus of this work is snow and glaciers in the mountains around the globe. Candidates with
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insights. Ability to apply system-level thinking, linking infrastructure performance, environmental conditions, and operations. Experience with data-driven modelling or machine learning. Ability to work both
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a wide array of family-friendly and cultural programs to eligible team members. Learn more at: https://hr.duke.edu/benefits/ Equal Opportunity Employer: Duke is an Equal Opportunity Employer
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data and deep learning methods to assess canopy cover, quality, carbon stocks, and ecosystem services. Mandatory requirements: PhD in areas related to forest resources, remote sensing, data science, or
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to topics such as natural language processing and machine learning; computational social science; human-AI collaboration and societal impacts of AI; or AI safety, governance, policy, and cybersecurity. All
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, the following position is available from 1st January 2027: PhD Position in Learning Analytics and Self-Regulated Learning (m/f/d, E13 TV-L, 75%) DFG-funded project TRACE: Trajectories of Adaptation, Disengagement
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doctoral degree. More about being a doctoral student at LTH on lth.se. https://www.lth.se/english/study-at-lth/phd-studies/ AEGIS — Adaptive Grid-Interactive Edge Datacenter Fleets is a European Marie
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equivalent foreign degree, obtained within the last three years prior to the application deadline Experience with simulation frameworks, system-level performance evaluation, or machine learning, is highly
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expansion project. All applicants whose work would contribute to advancing biological knowledge through machine learning or other quantitative/computational methods are encouraged to apply, including those