Postdoctoral Researcher in Multimodal Human Sensing and Advanced Behavioral Data Analysis
On-site · Lausanne, Vaud, Switzerland
Job Summary
Lead the technical and quantitative core of the SNSF-funded multimodal sensing project. Responsibilities include developing, implementing, and optimizing immersive behavioral tasks in VR; integrating behavioral task events with physiological acquisition, movement tracking, and experimental logs; establishing robust synchronization, calibration, and quality-control procedures across multimodal data streams; troubleshooting software, hardware, sensors, timing, and data acquisition; extracting and analyzing multimodal behavioral features from head, hand, body, and positional tracking; processing physiological signals (ECG, HRV, electrodermal activity, respiration, cortisol); developing reproducible data-preprocessing, feature-extraction, statistical modeling, visualization, and documentation pipelines; implementing advanced statistical analyses (trial-level models, mixed-effects, clustering, dimensionality reduction, predictive modeling, cross-validation, interpretable feature analysis); integrating behavioral, kinematic, physiological, endocrine, and questionnaire-based measures to characterize individual differences in motivation and stress responsiveness; contributing to experimental design, pilot testing, participant testing, manuscript preparation, conference presentations, and open-science deliverables; supervising students and contributing to training of junior lab members.
Required Qualifications
- PhD in biomedical engineering, electrical engineering, computer science, data science, computational neuroscience, human movement science, psychophysiology, cognitive neuroscience, psychology (or a closely related discipline) with strong quantitative expertise
- Strong programming skills (Python and/or R) with reproducible data-analysis workflows
- Excellent quantitative and statistical reasoning; understanding of model assumptions, uncertainty, validation, data structure
- Experience with multimodal human behavioral data, time-series data, sensor-based data, physiological signals, movement tracking
- Knowledge of Unity game engine development and network programming with C# or equivalent at sufficient level to maintain data acquisition setup
- Experience with physiological data acquisition and/or signal processing (ECG, HRV, electrodermal activity, respiration, wearable sensors)
- Ability to troubleshoot complex experimental setups involving software, hardware, sensors, timing, synchronization, and data acquisition
- Experience with advanced statistical or computational methods (mixed-effects models, hierarchical models, Bayesian models, trial-level analyses, dimensionality reduction, clustering, latent profiles, predictive modeling, model comparison)
- Ability to build robust pipelines rather than simply apply standard analysis packages
- Strong interest in human behavior, motivation, stress, individual differences, and quantitative behavioral neuroscience
- Excellent organizational, communication, and documentation skills
- Prior VR experience (welcomed but not required)
- Relevant experience may include real-time interactive systems, Unity/C#, motion capture, robotics, wearable sensors, synchronization of multimodal data streams
Desired Qualifications
- Strong programming skills in Python and/or R
- Experience with multimodal human behavioral data
- Signal processing
- Bayesian or hierarchical modeling
- Experiment design and data acquisition
- Unity/C# or equivalent
- VR experience
- Open-science practices
- Excellent organizational, communication, and documentation skills
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