Neuroscience PhD Student

Paris
Analytics and Machine Learning – Device Algorithms /
On-site
Beacon Biosignals is on a mission to revolutionize precision medicine for the brain. We are the leading at-home EEG platform supporting clinical development of novel therapeutics for neurological, psychiatric, and sleep disorders. Our FDA 510(k)-cleared Dreem EEG headband and AI algorithms enable quantitative biomarker discovery and implementation. Beacon’s Clinico-EEG database contains EEG data from nearly 100,000 patients, and our cloud-native analytics platform powers large-scale RWD/RWE retrospective and predictive studies. Beacon Biosignals is changing the way that patients are treated for any disorder that affects brain physiology.

As part of an industry-sponsored PhD at Beacon, you'll work alongside data scientists, neuroscientists, engineers, clinicians, and the University of Montpellier to improve the phenotyping of various central nervous system diseases before and after treatment during your PhD thesis. You’ll have the opportunity to collaborate with one of the leading scientists for central nervous disorder disease and leverage Beacon's hardware, machine-learning, and analytical capabilities to gather and analyze unique datasets collected in real-world settings to assess the impact of various therapies on EEG and sleep.

PhD Topic: Phenotyping of central nervous system disease with a longitudinal characterization of EEG macro and micro features with treatment.
Using Beacon’s Dreem Headband, you will collaborate with the University of Montpellier to collect longitudinal data from subjects with narcolepsy, IH and AD disorders. You will support data collection and monitoring, and perform data ingestion and curation. You will also work on analysis of retrospective EEG data, leveraging Beacon’s machine learning algorithms, to develop micro and macro features supporting subjects' phenotyping in various cohorts. Once the data acquisition and the clinical trial are finished, you will use the micro and macro features you developed and apply them to the clinical trial data to evaluate treatment-related changes in phenotype across the  longitudinal data.

Beacon's robust asynchronous work practices ensure a first-class remote work experience, but we also have in-person office hubs available located in Boston, New York and Paris.

What success looks like:

    • Support data collection and monitoring of a live clinical study in patients with CNS disease
    • Perform data ingestion and curation to prepare data for analysis
    • Leverage the Beacon data platform to ingest and visualize complex datasets
    • Use Beacon machine-learning-powered analytics to analyze retrospective and prospective data and build EEG features to phenotype CNS disorders
    • Use Beacon Machine Learning infrastructure to train or evaluate deep learning models tailored to your needs
    • Present and publish your key results in papers and conference presentations.

What you will bring:

    • You are very interested in neuroscience and the impact it can have on people's lives
    • You want to work in a highly cross-disciplinary environment with MD, software engineers, neuroscientists, ML engineers
    • You have a Master of Science in Computational Neuroscience or in a scientific field
    • You are familiar with Python, R or Julia
    • You are familiar with how to perform statistical analysis of large datasets
    • You have already handled EEG or biosignals data at scale and are familiar with the steps required to process and standardize them
    • You have already used Machine Learning through your classes, projects, or in a professional setting, ideally on EEG data
At Beacon, we've found that cultural and scientific impact is driven most by those who lead by example as such, we're always seeking out new contributors whose work demonstrates innate curiosity, a bias toward simplicity, an eye for composability, a self-service mindset, and—most of all—a deep empathy toward colleagues, stakeholders, users, and patients. We believe a diverse team builds more robust systems and achieves higher impact.