<div>Can you identify birds, bats or frogs from acoustic data?    Or can you identify plants or mammals from high-resolution images?    Do you enjoy sharing your expertise and improving the way the world measures biodiversity?    About the role: This is a paid role that you can do online, remotely from anywhere, at any time. We provide project-based work, ranging from 5 - 40 hours per week, based on your availability. The role involves adding species labels to the audio or image files to name the species present. Your expertise will help us protect nature by providing robust evidence. Together, we will build the most detailed set of annotated, species-level biodiversity data ever compiled.  Role responsibilities:   <ul><li>Identification of species from coastal Republic of Congo, or nearby countries in West or Central Africa, through high-resolution images (plants and/or mammals) or acoustic data (birds, bats, and/or frogs) shared online.  </li><li>Support the design of biodiversity surveys and help train machine learning models by annotating image and/or acoustic data. </li><li>Provide feedback on data collection methods to improve automated identification using machine learning </li></ul> How does it work?   In 3 easy steps:  1) Please fill out <a href="https://s.pivotal.earth/join-talentdesk">this application form</a> – this should take you 2-4 minutes.  2) We will invite you to do a short trial exercise to demonstrate your expertise.3) We will agree an hourly rate of pay that best suits your needs and experience.  You are now ready to start working with us!   About Pivotal and our Expert Network:  We are a UK-based biodiversity data and analytics company who believe good business must be good for nature. Our mission is to provide real nature intelligence to create a better future. ‍ We capture biodiversity data on the ground using a range of remote sensing technologies. All the data we collect is organised and analysed first with machine learning, then quality-checked by ecosystem experts to provide measured, quality-controlled analytics to show evidence of changes in nature and biodiversity on the ground.  We currently work with over 2,000 leading ecology experts around the world and are looking to continue adding to our global network of experts to support our mission of protecting and restoring biodiversity. ‍ You can read about two of the experts in our network on our blog: <a href="https://pivotal.earth/insights/meet-the-ecosystem-experts/">https://pivotal.earth/insights/meet-the-ecosystem-experts/</a> ‍ Please feel free to email us directly with any questions: <a href="mailto:annotator-support@pivotal.earth">annotator-support@pivotal.earth</a>. </div>

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<div id="ember55" class="ember-view organization-about-module__content-consistant-cards-description">We believe good business must be good for nature. We&rsquo;re building an economy that puts nature and&nbsp;business in balance. An economy for our future &ndash; where there&rsquo;s always a positive exchange.</div>
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Ecologists, Naturalists, and Taxonomic Experts for the Republic of Congo (remote / flexible / part-time role)

<div>Can you identify birds, bats or frogs from acoustic data?  &nbsp;&nbsp;Or can you identify plants or mammals from high-resolution images?  &nbsp;&nbsp;Do you enjoy sharing your expertise and improving the way the world measures biodiversity?&nbsp;&nbsp;&nbsp;&nbsp;About the role: This is a paid role that you can do online, remotely from anywhere, at any time. We provide project-based work, ranging from 5 - 40 hours per week, based on your availability. The role involves adding species labels to the audio or image files to name the species present. Your expertise will help us protect nature by providing robust evidence. Together, we will build the most detailed set of annotated, species-level biodiversity data ever compiled.&nbsp; Role responsibilities: &nbsp;&nbsp;<ul><li>Identification of species from coastal Republic of Congo, or nearby countries in West or Central Africa, through high-resolution images (plants and/or mammals) or acoustic data (birds, bats, and/or frogs) shared online.&nbsp;&nbsp;</li><li>Support the design of biodiversity surveys and help train machine learning models by annotating image and/or acoustic data.&nbsp;</li><li>Provide feedback on data collection methods to improve automated identification using machine learning </li></ul> How does it work? &nbsp; In 3 easy steps:&nbsp; 1) Please fill out <a href="https://s.pivotal.earth/join-talentdesk">this application form</a> – this should take you 2-4 minutes.&nbsp; 2) We will invite you to do a short trial exercise to&nbsp;demonstrate your expertise.3) We will agree an hourly rate of pay that best suits your needs and experience. &nbsp;You are now ready to start working with us!&nbsp;&nbsp; About Pivotal and our Expert Network:&nbsp; We are a UK-based biodiversity data and analytics company who believe good business must be good for nature. Our mission is to provide real nature intelligence to create a better future.&nbsp;‍&nbsp;We capture biodiversity data on the ground using a range of remote sensing technologies. All the data we collect is organised and analysed first with machine learning, then quality-checked by ecosystem experts to provide measured, quality-controlled analytics to show evidence of changes in nature and biodiversity on the ground.&nbsp;&nbsp;We currently work with over 2,000 leading ecology experts around the world and are looking to continue adding to our global network of experts to support our mission of protecting and restoring biodiversity.&nbsp;‍&nbsp;You can read about two of the experts in our network on our blog: <a href="https://pivotal.earth/insights/meet-the-ecosystem-experts/">https://pivotal.earth/insights/meet-the-ecosystem-experts/</a>&nbsp;‍&nbsp;Please feel free to email us directly with any questions: <a href="mailto:annotator-support@pivotal.earth">annotator-support@pivotal.earth</a>.&nbsp;</div>

Ecologists, Naturalists, and Taxonomic Experts for the Republic of Congo (remote / flexible / part-time role)

About the Job

About the Company

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