Postdoctoral Fellow in Machine Learning
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 swine 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.
Skills and Competencies Required:
- Excellent analytical and troubleshooting skills,
- Demonstrated knowledge of software development methodologies and software pipeline design.
- Ability to solve complex and large-scale problems to make important contributions to medicine and science.
- Strong software engineering and quantitative background including knowledge in Python, Unix Shell (e.g. Bash, Zsh, etc), deep neural networks of different architectures (convolutional, recurrent, etc), algorithms (sorting, binary search, etc), C++ or any other compiler based language(s), calculus, basic statistics (hypothesis testing, distributions, regression, etc), data visualization, etc.
- Experience in scientific method and critical thinking
- Detail-oriented and pro-active workstyle
- Strong ethical principles
- Ability to work independently and as part of a team
- Excellent verbal and written communication skills
Any additional skills are a plus including:
Parallel computing, command prompt, working with GPUs, supercomputing, video software (e.g. ffmpeg), SQL, large language models, MATLAB, PHP, or other additional languages, hardware knowledge, advanced understanding of kernel, neurobiology knowledge, etc.
The person will interact with staff in the labs of Sydney Cash, Kevin Staley, and Kyle Lillis.