Society for Neuroscience
Machine Learning Engineer/ Postdoctoral Fellow
Society for Neuroscience, Boston, Massachusetts, us, 02298
Machine Learning Engineer/ Postdoctoral Fellow
Employer:
Brain Trauma Lab, Massachusetts General HospitalLocation:
Charlestown, MassachusettsSalary:
Commensurate with experienceClosing date:
Oct 30, 2024Our laboratory applies computational and machine learning methods to investigate the impact of seizures and abnormal brain activity on outcomes in pigs with cortical impact. Our goal is to understand pathological correlates of epilepsy and traumatic brain injury. Analysis of datasets (including video-EEG telemetry, intracellular Chloride, among others) is central to these efforts.Specific efforts focus on developing methods for automatically classifying the semiology of pigs in video monitoring as they undergo the development of epilepsy and understanding the relationships between any abnormal behaviors and time after injury or the change in seizure frequency. Efforts will particularly focus on using supervised machine learning approaches including training artificial neural networks via open source software such as Keras, Tensorflow, DeepLabCut, SimBA, TREBA etc. or unsupervised learning methods, heuristics, and other algorithms to learn patterns, fit and extrapolate from models, and process large datasets of video frames.The person will interact with staff in other labs such as Sydney Cash's lab, Kevin Staley's lab, and Kyle Lillis' lab.The machine learning engineer will work and mentor a team of researchers in searching for patterns hidden in large data sets for research in neurology. The machine learning engineer will be responsible for data from the electronic data repository, including EEG, video, and peripheral blood biomarkers. The machine learning engineer will develop unique algorithmic approaches for analysis of data and supervise and mentor a team of research staff. Responsibilities will include:Creating or applying methods for automatic classification or regression on large dataSoftware development and code managementData wrangling of biological, instrumental, or technical dataGuiding a team on computational tasks and helping oversee research staffProblem solving and troubleshooting of technical problems for research staffManagement of a large physiological database, warehouse, and/or repositoryDevelopment of algorithms and maintaining a software pipelineCollaborate and interface with personnel from other research laboratoriesDocumenting steps for reproducing resultsOutlining desired milestones for research staff so that objectives can be metGenerate reports of statistical analysisPrepare and submit research manuscripts and abstractsProvide weekly updates on data processing, analysis or other research progressPresent at lab meetings, and at local and national meetingsData annotation, storage, and managementCommunicating concepts in a helpful way to those that are not computer scientistsEducationBachelor's Degree required. Master's and/or PhD preferred in a relevant discipline such as: computer science, math, computer engineering, statistics, cognitive science, electrical engineering, bioengineering, data science, etc.ExperienceMinimum of 2 years of relevant experience required; Knowledge of some Computer Science/Engineering concepts required.
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Employer:
Brain Trauma Lab, Massachusetts General HospitalLocation:
Charlestown, MassachusettsSalary:
Commensurate with experienceClosing date:
Oct 30, 2024Our laboratory applies computational and machine learning methods to investigate the impact of seizures and abnormal brain activity on outcomes in pigs with cortical impact. Our goal is to understand pathological correlates of epilepsy and traumatic brain injury. Analysis of datasets (including video-EEG telemetry, intracellular Chloride, among others) is central to these efforts.Specific efforts focus on developing methods for automatically classifying the semiology of pigs in video monitoring as they undergo the development of epilepsy and understanding the relationships between any abnormal behaviors and time after injury or the change in seizure frequency. Efforts will particularly focus on using supervised machine learning approaches including training artificial neural networks via open source software such as Keras, Tensorflow, DeepLabCut, SimBA, TREBA etc. or unsupervised learning methods, heuristics, and other algorithms to learn patterns, fit and extrapolate from models, and process large datasets of video frames.The person will interact with staff in other labs such as Sydney Cash's lab, Kevin Staley's lab, and Kyle Lillis' lab.The machine learning engineer will work and mentor a team of researchers in searching for patterns hidden in large data sets for research in neurology. The machine learning engineer will be responsible for data from the electronic data repository, including EEG, video, and peripheral blood biomarkers. The machine learning engineer will develop unique algorithmic approaches for analysis of data and supervise and mentor a team of research staff. Responsibilities will include:Creating or applying methods for automatic classification or regression on large dataSoftware development and code managementData wrangling of biological, instrumental, or technical dataGuiding a team on computational tasks and helping oversee research staffProblem solving and troubleshooting of technical problems for research staffManagement of a large physiological database, warehouse, and/or repositoryDevelopment of algorithms and maintaining a software pipelineCollaborate and interface with personnel from other research laboratoriesDocumenting steps for reproducing resultsOutlining desired milestones for research staff so that objectives can be metGenerate reports of statistical analysisPrepare and submit research manuscripts and abstractsProvide weekly updates on data processing, analysis or other research progressPresent at lab meetings, and at local and national meetingsData annotation, storage, and managementCommunicating concepts in a helpful way to those that are not computer scientistsEducationBachelor's Degree required. Master's and/or PhD preferred in a relevant discipline such as: computer science, math, computer engineering, statistics, cognitive science, electrical engineering, bioengineering, data science, etc.ExperienceMinimum of 2 years of relevant experience required; Knowledge of some Computer Science/Engineering concepts required.
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