Brain Trauma Lab, Massachusetts General Hospital
Machine Learning Engineer/ Postdoctoral Fellow
Brain Trauma Lab, Massachusetts General Hospital, Boston, Massachusetts, us, 02298
Our 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 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 data
Software development and code management
Data wrangling of biological, instrumental, or technical data
Guiding a team on computational tasks and helping oversee research staff
Problem solving and troubleshooting of technical problems for research staff
Management of a large physiological database, warehouse, and/or repository
Development of algorithms and maintaining a software pipeline
Collaborate and interface with personnel from other research laboratories
Documenting steps for reproducing results
Outlining desired milestones for research staff so that objectives can be met
Generate reports of statistical analysis
Prepare and submit research manuscripts and abstracts
Provide weekly updates on data processing, analysis or other research progress
Present at lab meetings, and at local and national meetings
Data annotation, storage, and management
Communicating concepts in a helpful way to those that are not computer scientists
Education
Bachelor’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.
Experience
Minimum of 2 years of relevant experience required. Knowledge of some Computer Science/Engineering concepts required.
#J-18808-Ljbffr
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 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 data
Software development and code management
Data wrangling of biological, instrumental, or technical data
Guiding a team on computational tasks and helping oversee research staff
Problem solving and troubleshooting of technical problems for research staff
Management of a large physiological database, warehouse, and/or repository
Development of algorithms and maintaining a software pipeline
Collaborate and interface with personnel from other research laboratories
Documenting steps for reproducing results
Outlining desired milestones for research staff so that objectives can be met
Generate reports of statistical analysis
Prepare and submit research manuscripts and abstracts
Provide weekly updates on data processing, analysis or other research progress
Present at lab meetings, and at local and national meetings
Data annotation, storage, and management
Communicating concepts in a helpful way to those that are not computer scientists
Education
Bachelor’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.
Experience
Minimum of 2 years of relevant experience required. Knowledge of some Computer Science/Engineering concepts required.
#J-18808-Ljbffr