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., Pettersson, H., Behrens, A., Männik A., 2018. Comparing a 41-year model hindcast with decades of wave measurements from the Baltic Sea. Ocean Engineering, 152, 57–71. https://doi.org/10.1016/j.oceaneng
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UPOs PhD enrolment: Université Paris Cité DC15: Hybrid machine learning models for data-driven bioprocess optimisation PhD enrolment: University of Padua Eligibility Requirements: Doctoral Candidates
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) techniques, numerous studies have explored replacing traditional constitutive models with black-box neural networks or other data-driven approaches. However, it has been shown that such black-box models may
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fundamental problems in biology and work towards foundation models of biological systems and innovative AI-driven biotech applications. The Laboratory of Computational Biology in Leuven (www.aertslab.org ), led
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, development, and evaluation of a digital twin model for on-site, renewable-driven green hydrogen generation systems. The successful candidate will contribute to an industry-sponsored applied research project
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, transport processes, numerical modeling, and network theory. Familiarity with plant hydraulics (xylem/phloem transport), osmotically driven flow, and biomechanical modeling. Demonstrated experience in
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simulations and data-driven models for optimizing electrolyte formulation. The project involves close interaction with experimental partners in the AFLOW consortium and includes a planned research stay at
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traditional predictive attempts and limits the availability of training data for high-resolution atmospheric and hydrological models. This limitation is compounded by the fact that many atmospheric reanalysis
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and validation of a predictive pipeline for excipient–biologic interactions Integration of experimental SAXS data with AI-driven structural modeling to predict oligomerization behavior and excipient
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The University of Virginia Department of Mechanical and Aerospace Engineering invites applications for a Postdoctoral Research Associate focused on structure design to address critical healthcare