Search for University Jobs in Sciences

The San Diego Zoo Wildlife Alliance Conservation Technology Lab seeks a postdoctoral associate for a three-year position applying their machine learning expertise to bioacoustics problems in ecology and conservation.

The problems include identifying species in acoustically complex field recordings from passive acoustic recorders for biodiversity monitoring and other ecological studies; improving methods for unsupervised, semi-supervised, and/or active learning-based preparation of such data; evaluating, refining, and designing tools for managing and analyzing data from passive acoustic recorders; helping guide the design of TinyML acoustic models for use in embedded devices; researching classification (in some taxa) to intraspecific class (e.g. chick/juvenile/adult) or, aspirationaly, individual level; and investigating optimal alignment between cutting-edge ML capabilities and the requirements of ecological methods commonly used in conservation.   

The work will make use of existing multi-year datasets and its outputs will contribute to projects across the SDZWA Global Hubs including in the Peruvian Amazon, in Cameroon's Ebo Forest, in the savannas of Kenya, with burrowing owls and other grassland species in the US Southwest, and with polar and panda bears.  

The desired candidate has a doctoral degree in computer science or a related field with a focus in machine learning (ML), with a preference for candidates with experience specifically in applying ML to acoustic data.  An ideal candidate already has experience in bioacoustics and/or passive acoustic monitoring.  Qualified candidates will be strong Python programmers; be highly skilled in the NumPy/SciPy/pandas/scikit-learn toolset; be advanced users of an ML framework such as PyTorch or TensorFlow and associated tools connected to audio processing like librosa; and possess in-depth knowledge about contemporary methods in machine learning.  Experience with R is a plus. The selected candidate will work with a diverse group of collaborators within SDZWA and also with our local and global partners, in particular with our faculty collaborators at UCSD and SDSU who specialize in machine learning and bioacoustics.