Academic Research

Most animal species on Earth remain unknown, particularly among the small insects that dominate animal diversity. At the Center for Integrative Biodiversity Discovery (CIBD) in Berlin, we are developing ways to turn species discovery from a largely manual process into something that can operate at the scale of biodiversity itself. We combine classical taxonomy with robotics, high-throughput imaging, nanopore DNA barcoding, artificial intelligence and bioinformatics. Our DiversityScanner, for example, automatically handles individual insects from bulk samples, photographs them and prepares them for downstream molecular work: https://youtu.be/ElJ5VSHa4OI. 
DNA barcodes then help organize specimens into putative species and reveal which species may still be unknown. With ONTbarcoder, DNA barcodes can even be generated while a nanopore sequencing run is still in progress: https://youtu.be/nFl8Vw2euMA.
Images, DNA and morphology can subsequently be combined for integrative species descriptions and used to train AI identification tools. Students therefore get to follow the path from an anonymous insect in a biodiversity sample to a documented species hypothesis and, in suitable cases, material for a formal species description.
The larger goal is to make discovery, identification and monitoring of the largely unknown part of animal diversity faster, more scalable and globally accessible.

Subject Areas and Working Fields:

Interns will participate in an integrative species-discovery workflow rather than work on an isolated laboratory exercise. Depending on their interests and experience, they may work with robotic specimen handling, high-throughput imaging, morphological sorting, nanopore DNA barcoding, sequence analysis, AI-assisted identification and taxonomic research.
A typical project could start with a bulk insect sample and progress through imaging and DNA barcoding to clustering specimens into putative species, selecting representative vouchers and determining which species are already known. Particularly interesting specimens can then be studied morphologically and compared with taxonomic literature, sequence databases and natural-history collections. Interns may also help generate image and barcode datasets for training AI identification models or biodiversity monitoring. Where suitable material is available, the work may contribute directly to ongoing integrative species descriptions.
Up to three interns can work on complementary samples or taxonomic groups while sharing methods, equipment and results.

Advanced undergraduate students (from the second year)

Master’s students

Doctoral candidates

We are looking for students who are curious about biodiversity and enjoy combining biological questions with new technologies. Previous experience in entomology, taxonomy, molecular biology, microscopy, bioinformatics, imaging, R or Python is useful but not required. More important are careful working habits, willingness to learn unfamiliar methods, and an interest in moving between laboratory work, specimens and data analysis.
Interns should be able to work independently after training while also collaborating with an interdisciplinary and international team. Attention to detail and good documentation are particularly important because the projects involve real specimens and research data.

No specialized preparation is required. Selected interns will receive a short reading list and project-specific background material before arrival. Familiarity with basic molecular biology, microscopy, R or Python can be useful but is not essential.