Bioscope
What You'll Do
Develop Novel Multimodal AI Systems:
Design and implement state-of-the-art models that integrate time series data from wearables, CGM, etc. with LLM-based reasoning and analysis
Longitudinal Patient Analysis:
Build systems to analyze and interpret patient health trajectories over time, identifying patterns, anomalies, and clinically relevant insights from continuous monitoring data
Bridge Temporal and Linguistic Modalities:
Create architectures that effectively combine sequential sensor data with natural language medical records, clinical notes, and knowledge bases
Model Development & Research:
Stay at the forefront of multimodal AI research, implementing and adapting the latest techniques in time series forecasting, representation learning, and transformer-based models
Clinical Collaboration:
Work closely with healthcare professionals to understand clinical needs and translate them into technical solutions
Production Systems:
Deploy robust, scalable AI systems that handle real-world patient data with appropriate attention to privacy, security, and regulatory requirements
Technical Focus Areas
Time series analysis and forecasting from wearable devices (heart rate, activity, sleep patterns, etc.)
Integration of LLMs with temporal biomedical data
Multimodal representation learning and fusion techniques
Anomaly detection and pattern recognition in longitudinal health data
Temporal reasoning and causal inference from observational data
Signal processing and feature extraction from continuous monitoring devices
Qualifications Required
Master's degree
in Computer Science, Machine Learning, Biomedical Engineering, Statistics, or related quantitative field ( PhD preferred )
Strong foundation in deep learning frameworks (PyTorch)
Experience with foundation models and Large Language Models (transformers, pre-training, fine-tuning)
Demonstrated experience with time series analysis and forecasting methods
Experience putting deep learning models into production environments
Proficiency in Python and modern ML development tools
Strong understanding of machine learning fundamentals and statistics
Ability to read and implement research papers
Excellent communication skills and ability to work collaboratively
Preferred
Publications in top-tier ML/AI conferences: NeurIPS, ICML, ICLR, KDD, etc.
Experience with healthcare data: EHR, wearables
Background in signal processing or biosignal analysis
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Develop Novel Multimodal AI Systems:
Design and implement state-of-the-art models that integrate time series data from wearables, CGM, etc. with LLM-based reasoning and analysis
Longitudinal Patient Analysis:
Build systems to analyze and interpret patient health trajectories over time, identifying patterns, anomalies, and clinically relevant insights from continuous monitoring data
Bridge Temporal and Linguistic Modalities:
Create architectures that effectively combine sequential sensor data with natural language medical records, clinical notes, and knowledge bases
Model Development & Research:
Stay at the forefront of multimodal AI research, implementing and adapting the latest techniques in time series forecasting, representation learning, and transformer-based models
Clinical Collaboration:
Work closely with healthcare professionals to understand clinical needs and translate them into technical solutions
Production Systems:
Deploy robust, scalable AI systems that handle real-world patient data with appropriate attention to privacy, security, and regulatory requirements
Technical Focus Areas
Time series analysis and forecasting from wearable devices (heart rate, activity, sleep patterns, etc.)
Integration of LLMs with temporal biomedical data
Multimodal representation learning and fusion techniques
Anomaly detection and pattern recognition in longitudinal health data
Temporal reasoning and causal inference from observational data
Signal processing and feature extraction from continuous monitoring devices
Qualifications Required
Master's degree
in Computer Science, Machine Learning, Biomedical Engineering, Statistics, or related quantitative field ( PhD preferred )
Strong foundation in deep learning frameworks (PyTorch)
Experience with foundation models and Large Language Models (transformers, pre-training, fine-tuning)
Demonstrated experience with time series analysis and forecasting methods
Experience putting deep learning models into production environments
Proficiency in Python and modern ML development tools
Strong understanding of machine learning fundamentals and statistics
Ability to read and implement research papers
Excellent communication skills and ability to work collaboratively
Preferred
Publications in top-tier ML/AI conferences: NeurIPS, ICML, ICLR, KDD, etc.
Experience with healthcare data: EHR, wearables
Background in signal processing or biosignal analysis
#J-18808-Ljbffr