Research Internship in Machine Learning for fossil data analysis - University of Helsinki

Fossils are preserved remains or traces of animals, plants, and other organisms
from the remote past. The fossil record gives clues about the history of life and
environmental changes over millions of years, it helps to understand
environmental change, biological and ecological processes and can be used to
reconstruct past climate and human impact on the environment.
Fossil record is “big data” in a sense that it comes from many different sources, it
is biased due to how fossils preserve and how they are collected, identification is
interpretation based and uncertain in many ways, records are of varying quality
over time.
We are looking for a student/early career researcher with a background in
machine learning/ data mining and interest in life science applications to join a
research project on better understanding fossil data. The goal is to analyze
potential biases in fossil data and develop methods for correction using machine
learning techniques. From the machine learning perspective, the task is to design
and implement active learning algorithms (preferably in R or Python) for modeling
fossil collection biases via a crowd-sourcing experiment (a simple web-app to be
developed). The project offers an opportunity to do a Master’s thesis, but it is
not mandatory.
We offer a full-time employment for 3-4 months during the summer with a
possibility to continue on a part time basis in autumn. The position is at the
University of Helsinki jointly at the Department of Computer Science and the
Department of Geosciences and Geography, Kumpula campus, Helsinki.
If you are interested, please contact Indre Zliobaite
zliobaite-AT-gmail com by March 1 or earlier. Applications will be considered
as they come until the position is filled.
More about our research: http://www.zliobaite.com/fossils
More about fossils: http://africanfossils.org/
More about where fossils come from: http://fossilfinder.org/