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NIST only participates in the February and August reviews. In many application areas, materials development increasingly involves manipulating the local atomic order to optimize properties
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for homogeneous photocatalysis. Physico-chemical characterization Structural, optical, and electronic analysis of materials (spectroscopies, microscopies, electrochemistry, etc.) to establish structure–property
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) nanosheets for low-pressure hydrogen storage applications. This position involves close collaboration with academic and industrial partners to design innovative nanomaterials and optimize their performance
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on methodological development in cryo-electron microscopy (cryo-EM), particularly in image reconstruction and 3D volumetric analysis of macromolecular structures. Rather than aiming to incrementally optimize existing
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optimization of technical processes and products. We present the exciting range of applications of our collaborative software development MESHFREE: https://www.meshfree.eu/ Currently, our team is working
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ALMA MATER STUDIORUM - UNIVERSITA' DI BOLOGNA - - DIPARTIMENTO DI INGEGNERIA CIVILE, CHIMICA, AMBIENTALE E DEI MATERIALI | Italy | 11 days ago
. Structural optimization and numerical modeling. Where to apply Website http://www.unibo.it Requirements Additional Information Eligibility criteria to apply for research grants fill out the form available
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, nanostructuring, porosity) and their surface/interface physico-chemistry (functionalization, topography) in order to modulate or optimize their properties. The heterogeneous and complex structure of the studied
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for membrane biophysics and drug‑delivery research. While conventional small‑angle scattering (SANS/SAXS) provides exquisite nanoscopic structural detail, it rarely captures the crucial mesoscale dynamics
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extensive electromagnetic modeling to optimize the waveguide structures for minimal loss, efficient confinement, and effective mode matching with external optical components. Particular attention will be
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and Machine Learning, with a focus on studying geometric structures in data and models and how to leverage such structure for the design of efficient machine learning algorithms with provable guarantees