Luis Moliner Cachazo
2024-2025: Postdoctoral researcher working on freshwater eDNA metabarcoding integrated with Community Science.
I have had the privilege of supervising students at various career stages, from MSc projects to PhD dissertations and postdoctoral research. My projects tend to focus on combining traditional taxonomic and ecological approaches with cutting-edge molecular and computational methods.
2024-2025: Postdoctoral researcher working on freshwater eDNA metabarcoding integrated with Community Science.
2017-2018: Postdoctoral researcher specializing in Southern African freshwater biodiversity through digitisation and DNA barcoding.
2020-2024: PhD graduate who studied ecological assembly rules in aquatic-terrestrial transition zones of the Okavango Delta.
2016-2021: PhD graduate who explored climate change impacts on aquatic insect body size and ecology across space and time.
2021-2025: PhD candidate applying computer vision to natural history collections for ecological and conservation research.
2021-ongoing: PhD candidate developing methods to assess riparian ecosystem health through interdisciplinary approaches.
2025: MSc student developing AI-driven identification systems for beetle collections using machine learning and morphological analysis.
2025: MSc student developing AI approaches for taxonomic identification of caddisfly larvae.
2023: MSc graduate who completed a taxonomic revision of the mantid genus Deroplatys, resulting in a published monograph.
2016: MSc graduate who investigated spatial and sexual differences in wing morphology of Calopteryx virgo using museum specimens.
2016: MSc graduate who applied digital visualization tools and morphometrics to analyze wing characteristics in the banded demoiselle, Calopteryx splendens.
Research Areas: My supervision covers diverse topics including molecular systematics, digital natural history, computer vision applications, freshwater ecology, climate change biology, and taxonomic research. I particularly encourage interdisciplinary approaches that combine traditional biological expertise with modern computational and molecular methods.