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                . Responsibilities Development and optimization of perovskite-based solar cells at different levels. Developing large-area perovskite solar cells utilizing KPV-LAB's baseline processes. Performing accurate device 
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                : In-situ testing facilities and characterization techniques (Generation of unique databases on well-identified frames), validated models in operational conditions (New models based on real in-situ 
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                project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict 
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                on the development of new methods integrating a variety of data types (remote sensing, geology, geophysics, geochemistry) for geological modelling and advanced exploration targeting of mineral deposits 
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                . Developing bottom cells at different scales (from 1 by 1 cm2 to 6 by 6 inch2) utilizing the PECVD-PVD cluster. Performing device characterization, and modeling aimed at champion PCEs. Manage project tasks 
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                on well-identified frames), validated models in operational conditions (New models based on real in-situ observations and mechanism). Microstructure manipulation for tailoring macroscopic response: Toughing 
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                on well-identified frames), validated models in operational conditions (New models based on real in-situ observations and mechanism). Microstructure manipulation for tailoring macroscopic response: Toughing 
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                on the development and implementation of new workflows for the mineralogical and geochemical characterization of ore materials, using combined µXRF-mapping, SEM-based automated mineralogy, and laser ablation ICP-MS 
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                intelligence framework for RO systems. Candidates with background in conventional and innovative membrane-based technologies with data driven modeling approach are encouraged to apply for this position. We 
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                of state-of-the-art bioinformatics pipelines; (ii) single-cell analysis, multi-omic analysis; (iii) spatial transcriptomics bioinformatic analysis. Learning and implementing Systems Medicine-based analysis