Postdoctoral Researcher in Multimodal Human Sensing and Advanced Behavioral Data Analysis

EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than 6,500 people supporting the three main missions of the institutions: education, research and innovation.

Mission

Swiss National Science Foundation (SNSF)-funded project on human motivation, stress physiology, immersive behavioral testing, and multimodal data analysis.

The Laboratory of Behavioral Genetics at EPFL, led by Prof. Carmen Sandi, is seeking an outstanding postdoctoral researcher with a strong technical and quantitative profile to join a SNSF-funded project investigating human motivation and stress responsiveness.

The project will develop and validate individually calibrated behavioral tasks in immersive virtual reality (VR) to quantify effort-based motivation, vigor, persistence and goal-directed versus habitual control. It will then test how acute stress alters these processes, combining behavioral performance, physiological monitoring, movement-based phenotyping, and advanced statistical and computational analyses.

Project background

Understanding human motivation requires methods that go beyond questionnaires and simplified computer-based tasks. This project aims to develop more naturalistic, yet highly controlled, behavioral assays in which participants make effort-based decisions and perform calibrated actions while physiological and movement data are continuously recorded.

Main responsibilities

The postdoctoral researcher will lead the technical and quantitative core of the project. Responsibilities will include:

• Developing, implementing, optimizing and troubleshooting immersive behavioral tasks.
• Integrating behavioral task events with physiological acquisition systems, movement tracking, positioning data and experimental logs.
• Establishing robust acquisition, synchronization, calibration and quality-control procedures across multimodal data streams.
• Troubleshooting software, hardware, sensors, timing, synchronization, data acquisition and experimental workflow issues.
• Extracting and analyzing multimodal behavioral features from head, hand, body and positional tracking data.
• Processing and analyzing physiological signals, including ECG / HRV, electrodermal activity, respiration and autonomic stress indices.
• Developing reproducible pipelines for data preprocessing, feature extraction, statistical modeling, visualization and documentation.
• Implementing advanced statistical and computational analyses.
• Integrating behavioral, kinematic, physiological, endocrine and questionnaire-based measures to characterize individual differences in motivation and stress responsiveness.
• Contributing to experimental design, task calibration, pilot testing, participant testing, manuscript preparation, conference presentations and open-science deliverables.
• Supervising students and contributing to the technical and quantitative training of junior lab members.

Candidate profile

Applicants should have a PhD in biomedical engineering, electrical engineering, computer science, data science, computational neuroscience, human movement science, psychophysiology, cognitive neuroscience, psychology with strong quantitative expertise, or a related discipline.

Essential qualifications include:

• Strong programming skills, preferably in Python and/or R.
• Excellent quantitative and statistical reasoning.
• Experience with multimodal human behavioral data, time-series data, sensor-based data, physiological signals, movement tracking, or related complex datasets.
• Some knowledge of Unity game engine development and experience with network programming with C# or equivalent.
• Experience with physiological data acquisition and/or signal processing.
• Ability to troubleshoot complex experimental setups.
• Experience with advanced statistical or computational methods.
• Strong interest in human behavior, motivation, stress, individual differences, and quantitative approaches to behavioral neuroscience.

Scientific environment

The successful candidate will join the Laboratory of Behavioral Genetics at EPFL, an interdisciplinary environment focused on the biological, behavioral and individual-difference mechanisms of stress, motivation, anxiety and resilience.

Application procedure

Interested candidates should send a single PDF file including:

• A cover letter describing their research background, technical and quantitative expertise and fit for this position.
• A detailed CV including a full list of publications.
• A brief research statement describing relevant previous work and methodological expertise.
• Contact details for three professional references.
• Optional: links to code repositories, analysis pipelines, experimental software, technical projects or other outputs.

Applications will be reviewed on a rolling basis until the position is filled. For any further information, please contact Prof. Carmen Sandi (carmen.sandi@epfl.ch).

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