Postdoctoral Fellowship in Machine Learning for Marine Protected Areas Detection

Position Overview:

The ComBi group at the LS2N—Nantes Université seeks a highly motivated and talented individual for a postdoctoral fellowship focused on developing Machine Learning algorithms to detect new Marine Protected Areas (MPAs). This interdisciplinary project is within the frame of Tara Océan. It combines marine biology, genomics, remote sensing, and artificial intelligence expertise to address pressing conservation challenges in the world's oceans.

Responsibilities:

The successful candidate will be responsible for:

- Designing and implementing novel Machine Learning algorithms for detecting potential Marine Protected Areas using metagenomic knowledge and satellite imagery.

- Integrating diverse datasets, including metagenomic data, environmental parameters, and satellite images, improves algorithm performance and accuracy.

- Collaborating with marine biologists, remote sensing experts, policymakers, and data scientists to validate and optimize the developed algorithms.

- Presenting results at national and international conferences in various disciplines.

Qualifications:

- A Ph.D. in Computer Science, Data Science, Bioinformatics, Marine Biology, or a related field.

- Strong programming skills in Python and experience with Machine Learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).

- Demonstrated expertise in developing and applying Machine Learning algorithms to environmental datasets.

- Experience working with genomic data, metagenomics, and/or satellite imagery is highly desirable.

- Excellent communication skills and the ability to work collaboratively in an interdisciplinary team environment.

Benefits:

The successful candidate will have the opportunity to:

- Work in a dynamic and collaborative research environment with leading marine biology, genomics, and artificial intelligence experts.

- Gain hands-on experience in cutting-edge research at the intersection of marine conservation and technology.

- Access state-of-the-art computational resources and facilities.

Application Process:

Interested candidates should submit the following application materials:

1. Curriculum vitae (CV), including a list of publications.

2. A cover letter outlining research interests, relevant experience, and career goals.

3. Contact information for two references.

About the Institution:

The ComBi team at LS2N is a leading research institution dedicated to modeling biological (eco)systems. As part of the Tara Ocean consortium, we emphasize ocean studies by developing new computational modeling frameworks. Situated in Nantes, our institution offers a vibrant and collaborative research environment with access to state-of-the-art facilities and resources. Join us in making a difference in marine conservation through cutting-edge Machine Learning research and innovation.