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of Chemistry and Materials Science, Aalto University, and the Data-driven Atomistic Simulation (DAS) group, led by Prof. Miguel Caro at the Department of Chemistry and Materials Science, Aalto University
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 2 months ago
simulations, and structural bioinformatics of proteins and other biomolecules – and we're now scaling that expertise to meet the volume and complexity of data these methods generate. This is not a conventional
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The Materials to Optoelectronic Devices (M2D) group ( https://www.m2ngroup.nl ) at the Eindhoven University of Technology (TU/e) is looking for a motivated PhD candidate to join an exciting research
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scanning and measurement, garment fit evaluation, cloth simulation, digital twins, wearable sensing, smart textiles, and AI methods for clothing and body data analysis, to name a few. Applicants from
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to an interview, you must bring certified copies of certificates and diplomas upon request. The application must include: Transcripts and diplomas for Bachelor's and Master's degrees CV Copy of Master's thesis
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | 3 months ago
-driven and physics-informed learning techniques Development of simulation-based optimization methodologies, including Bayesian optimization, derivative-free optimization, multi-objective optimization
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of molecules are not well understood. PhD candidate will be doing optical simulations and fabrication of devices for various types of testing, and will be involved in hands on optical and materials testing and
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, including industrial applications in geology. Your immediate leader will be the Head of Department. Duties of the position Simulate MRI data and model disturbances arising from various physical sources
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processing with light. Near a continuous phase transition, the susceptibility of a material can increase strongly. This allows weak input signals to generate large responses, which can in turn reduce the
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while maintaining computational efficiency through lower-fidelity simulation of non-critical regions, (ii) virtual sensing techniques for load and stress estimation from limited and optimally placed