Principal areas of interest include:

• Machine learning for bioacoustics, applying state of the art methods for species identification and soundscape modelling.
• Optimized sampling design to maximize the spatiotemporal efficiency of surveys.
• Improved acoustic data engineering including streamlined model tuning and validation processes and data compression.
• Scalable applications of emergent bioacoustic methods such as abundance estimation and individual recognition.

The fellow will be expected to:

• Lead in the development of these new capabilities.
• Develop and manage partnerships with relevant institutions related to these new technologies including existing collaborations with academic institutions.
• Be able to work independently in a variety of remote environments.
• Be able to work flexibly and collaboratively with multiple teams across different time-zones.
• Provide technical expertise and support grant writing and reporting activities.
• Provide mentorship to build capacity of NS and SDZWA young scientists.

To be successful, applicants must have the following expertise:

• Demonstrated excellence in applied bioacoustics for scientific research.
• Programming proficiency in Python and common packages including Numpy, Pandas, SciKit-Learn, and machine learning frameworks such as PyTorch or TensorFlow.
• Applications of acoustic classification algorithms such as PERCH and BirdNET or a strong background in neural networks.
• Familiarity with the principles of ecological monitoring and common analytical methods. Applicants with expertise in population or community ecology will be preferred.
• Experience using collaborative software tools with rigorous documentation.