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proficiency in integrating spatial data into 3D models using sophisticated interpolation techniques. Additionally, the postdoctoral researcher will contribute to transforming mining liabilities into valuable
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of errors between model predictions and post-operative reality This work will be carried out by the Biomécamot team (https://www.timc.fr/BiomecaMot ) at the TIMC laboratory, which is part of the CNRS's
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Scene Understanding Detection and Identification of Objects (SSUDIO) project. The purpose of this project is to develop scene understanding from 3D scans of ships by applying machine learning/computer
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breast cancer dormancy -Participate in the development of novel biomaterials to model in vitro breast cancer micro-units in controlled 2D and 3D microenvironments -Monitoring breast cancer cell dynamics
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, numerical methods, and Earth system modeling to develop and evaluate a coupled xylem–phloem transport framework that translates multiscale physics into next-generation vegetation model schemes. Key
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qualifications include (but are not limited to): Sophomore or Junior level Software development experience Microcontroller knowledge (i.e. Arduinos, Raspberry Pis, etc.) 3D modeling experience and knowledge of 3D
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natural fibers. 2.Design of 3D printing mixture. 3.Chemical treatment and pre-soaking RCA particles in an acid. 4. Deep learning modeling of parameters of cement composites. 5. Preparation of reports and
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(WP4), led by IEM, focuses on the design, modeling, and optimization of a laboratory-scale EC-MF pilot using promising electrocatalytic materials synthesized in WP1.3. The postdoctoral researcher will
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the integration of high data-density reaction/bioanalysis techniques, organic synthesis, laboratory automation & robotics and machine learning modelling. This exciting project involves the application of innovative
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part of PPPL's international collaboration with CEA, France. The final goal will be modeling self consistently plasma and impurity transport in the edge of a magnetic fusion device, tokamaks and