Call for applications
* Machine Learning / Computer Vision *
University of São Paulo (USP), BrazilApplication period: October/November, 2019
We have a postdoc position for plankton image processing and analysis, with focus on semi-supervised and user-interactive methods.
This opportunity is open to applicants of any nationality. The host project, World Wide Web of Plankton Image Curation (WWW.PIC), is an international project promoted by Belmont Forum, with participation of teams from France, United States, Brazil and Japan. The postdoc fellow will be part of the Brazilian team, will be primarily working at Institute of Mathematics and Statistics, and occasionally at the Oceanographic Institute, both at University of São Paulo, São Paulo, Brazil, and will have opportunities to interact with researchers from these countries. There is a fellowship from FAPESP for this position (see details below).
Research topic description
Modern imaging technologies allow acquisition of in situ plankton images in large scale. Recognizing and estimating their distribution on distinct taxonomic classes is of great importance for better understanding the marine ecosystem. Machine assisted methods for the recognition of these organisms are fundamental for timely processing and analysis of these data.
In this scenario, deep learning techniques emerge as a promising tool. However, the data-hungry nature of deep learning models is a challenge for their effective use, specially considering data that may have distinct characteristics depending on many factors such as season of the year, imaging technology, resolution, location where images are acquired, weather conditions, unknown species, and so on.
The main goal of the postdoc project is to develop machine learning based computational methods to speed up both annotation and classification of plankton images and at the same time minimize the required effort from the expert. That is, we do not wish to simply restart training for each new batch of data nor we would like to rely on experts manually labeling thousands of images for each situation. We would like to be able to effectively reuse previously generated knowledge to quickly produce a classifier adapted to the new imaging or use conditions.
The focus of the research should be on semi-supervised and user-interaction based methods. Clever user interaction to guide the data labeling and machine training processes and also for validating the results are desirable features for the solution. There is no strong constraint of approaches to be investigated as long as an effective solution is targeted. Developed methods will be integrated into EcoTaxa. As this project will be within an international collaboration, we expect to have access to distinct batch of images, coming from different equipment and imaging conditions.
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