Origin-XAI: Explainable AI-driven platform for rapid identification of interstellar origin of life

Objective

This project aims to develop an innovative methodology for identifying biogenic signatures in carbonate minerals by combining advanced spectroscopic techniques (Raman, NMR, IR) with artificial intelligence. The goal is to distinguish biotic from abiotic carbonates across modern, fossil, and experimental analogues. A key objective is to build a curated database of spectral features linked to organic compounds indicative of biogenicity. This dataset will be analyzed using numerical methods and machine learning to develop predictive models capable of identifying biosignatures and discriminating microbial from non-biological origins. Samples will include microbial lab precipitates, sediments from extreme modern environments (e.g., hypersaline, alkaline, acid), and well-preserved fossils The project will establish an AI-based framework for interpreting early biosignatures on Earth and guide future planetary exploration, especially on Mars, and other potentially habitable celestial bodies. The research will be hosted at the University of Granada (UGR) within the research group of Prof. Mónica Sánchez-Román, an expert within the field of geomicrobiology, sedimentary geochemistry, and astrobiology. Her work focuses on microbially driven carbonate precipitation in extreme environments, which serve as key analogues for early Earth and extraterrestrial habitability. The selected postdoctoral fellow will join an interdisciplinary team focused on microbial mineralization, biosignature detection, and early diagenetic and geobiological processes, contributing to cutting-edge research at the interface of geobiology, planetary science, and data-driven mineralogy, with innovative strategies for interpreting early life and supporting future planetary exploration missions.