Understanding and predicting the continuous change of the environment is crucial for scientists, economists, policy makers and ultimately for the entire society. Geochronology is the art of measuring the timing of processes on Earth and thus the key for understanding the past and making accurate predictions for the future. Techniques to model past and future events have evolved to an advanced state and geochronology has to keep track with this by providing accurate, precise and statistically robust age data. Fission-track dating is a well-established geochronological method, which is based on the manual counting and length measurement of nuclear damage tracks (i.e. fission tracks) in minerals by means of optical microscopy. Due to the complexity of microscopic images and objects to be studied, the operator-based optical counting remains the most widely applied approach until these days. However, the manual approach has serious limitations especially with respect to the number of grains being dated as well as the comparability and reproducibility of the results. This project is an attempt to automatize large parts of the slow and tedious manual procedure. It will combine fission-track dating with artificial-intelligence (AI)-assisted image analysis exploiting the capability of convoluted neural nets (the AI) to be ‘taught’ to detect user defined objects in an image. The expected result is a protocol that can be freely used and refined by all geochronology laboratories to produce age data meeting the high requirements of cutting-edge research. On the way of developing this protocol the experienced researcher will obtain hands-on training in the Python language and artificial intelligence, whereas the supervisor will get a deep insight into fission-track geochronology. The results will be communicated to a broad audience and provide a stable basis for future research inside and hopefully far outside geosciences thereby underlining the project’s societal importance.