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‑mining and machine‑learning methods. The expected scientific outcome is to establish guidelines for identifying and optimizing promising electrolyte materials and to support the development of future
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focuses on leveraging zebrafish as a model organism to develop and optimize genetic tools through a directed evolution pipeline, with significant therapeutic and industrial applications. Key
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algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics, spanning diverse application domains such as medicine, energy systems, biomedical
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to efficiently transform biochar into battery-grade hard carbons with controlled characteristics and optimized electrochemical performance. The use of microwave plasma carbonisation will allow
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innovative approaches to design and optimize materials with enhanced bioactivity. Experience in design and synthesis of peptides will be considered a strong merit, as well as expertise in chemical modification
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simulations and data-driven models for optimizing electrolyte formulation. The project involves close interaction with experimental partners in the AFLOW consortium and includes a planned research stay at