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Field
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Associate Support development and documentation of Python-based Design Tool workflow for D-Suite Project Formulate and solve optimization problems, developing algorithms which have appropriate accuracy
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leveraged to improve existing modulation models describing how large scales alter heat transfer. Optimal oscillations will be designed using reinforcement learning. Extending inner-outer interaction models
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-assisted simulation framework by providing accurate high-fidelity numerical data for training and validation of surrogate models for multi-disciplinary design and optimization. · Participating in
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methods (CASSFCF/NEVPT2). - Stage 2: Support in the analysis of molecular diffusion processes through membranes by means of molecular dynamics studies, oriented to optimize the response as MRI contrast
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superstructures. Optimize NPL ligand shell structure in collaboration with TU Dresden to improve optical performance of NPL superstructures. Extend existing single-particle photophysical models of NPLs
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Build and optimize workflows for analyzing biodiversity and entomological texts, focusing on functional traits, historical context, and geographic scope relevant to current environmental challenges
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, or erroneous data, Data cleaning and generation, Development of enhanced loss functions and information-theoretic methods for optimized data analysis, Machine learning-based image segmentation of tomographic
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coatings using techniques like electrodeposition and dip coating. •Develop and characterize porous TiO₂ and metal oxide thin films. •Establish and optimize 3D in vitro skin models for biological testing
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of research, in particular the design and development of multisensor acquisition and fusion architectures, applied artificial intelligence and optimization techniques in sectors such as energy, air traffic
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: Synthesis and optimization of PLGA-based nanoparticles and enzymatic nanomotors. Drug loading/release studies in cellular models. Development of patient-derived mucus-secreting tumor-on-a-chip model