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Postdoctoral Researcher in Multimodal Human Sensing and Advanced Behavioral Data Analysis

emploi.epfl.ch -
Lausanne, VD

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Détails de l'emploi

Qualifications

Niveau Doctorat
Dépannage logiciel
C#
R
Git
Recherche
Esprit d'analyse
Réseaux informatiques
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Linux
Compétences en communication
Python

Description complète du poste

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.

This position is part of a broader two-position recruitment linked to the same SNSF-funded project. The call is for the position focused on multimodal sensing, experimental systems, physiological and movement data, and advanced behavioral data analysis. A second, complementary position will focus more specifically on clinical and implementation aspects of the project.

Although the project uses virtual reality as an experimental platform, prior VR experience is not necessarily required. We are particularly interested in candidates with strong expertise in experimental systems, physiological sensing, signal processing, human movement analysis, computational neuroscience, biomedical engineering, data science, and/or advanced quantitative behavioral research.

The position is funded for three years. In line with standard EPFL procedures, the contract is issued on a one-year basis and renewable annually, subject to satisfactory progress and institutional regulations.

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.

Participants will complete immersive behavioral tasks designed to measure how they choose, initiate action, sustain effort, adapt to changing contingencies and respond to acute stress. The project will collect synchronized streams of trial-level behavioral data, head/hand/body positioning, movement trajectories, timing variables, ECG-derived heart rate and heart-rate variability, electrodermal activity, respiration, salivary cortisol and questionnaire-based individual-difference measures.

A central aim is to identify robust behavioral and physiological signatures of motivation and stress responsiveness, including individual profiles of response. The successful candidate will play a key role in ensuring that these complex data streams are acquired, synchronized, quality-controlled, modeled and interpreted with the highest level of technical and analytical rigor.

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 in close interaction with the PI and lab members.
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, including trial-level models, mixed-effects models, clustering or latent-profile analyses, dimensionality reduction, predictive modeling, cross-validation and 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, 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.

The ideal candidate will combine strong analytical ability with hands-on technical competence. We are looking for someone who can understand an experimental system from acquisition to analysis: how signals are generated, synchronized, cleaned, modeled, validated, and interpreted.

Essential qualifications include:

Strong programming skills, preferably in Python and/or R, with experience building reproducible data-analysis workflows.
Excellent quantitative and statistical reasoning, including a clear understanding of model assumptions, uncertainty, validation, data structure, and the limitations of different analytical approaches.
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, at sufficient level to maintain existing data acquisition setup.
Experience with physiological data acquisition and/or signal processing, ideally including ECG, heart-rate variability, electrodermal activity, respiration, wearable sensors, or related measures.
Ability to troubleshoot complex experimental setups involving software, hardware, sensors, timing, synchronization, and data acquisition.
Experience with advanced statistical or computational methods, such as mixed-effects models, hierarchical models, Bayesian models, trial-level analyses, dimensionality reduction, clustering, latent profiles, predictive modeling, or 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 approaches to behavioral neuroscience.
Excellent organizational, communication, and documentation skills.

Prior VR experience

Prior experience with virtual reality is welcome but not required. Candidates with strong backgrounds in experimental systems, human sensing, physiological acquisition, signal processing, movement analysis, computational modeling or advanced behavioral data analysis are strongly encouraged to apply.

Relevant experience may include real-time interactive systems, Unity/C#, motion capture, robotics, human-computer interaction, wearable sensors, experimental software, synchronization of multimodal data streams or other complex human experimental setups.

Additional strengths

Additional assets include experience with Biopac or comparable physiological acquisition systems; real-time data acquisition; motion capture or positional tracking; Bayesian or hierarchical modeling; reinforcement-learning models; effort-discounting or decision-making models; survival or hazard models; gradient-boosted decision trees or related predictive approaches; interpretable feature-importance methods; Git/GitHub; Linux or command-line workflows; preregistered analyses; and open-science practices.

Experience in stress research, motivation, affective neuroscience, computational psychiatry, neuroeconomics or human decision-making would be valuable, but the central requirement is methodological depth and the capacity to handle complex multimodal data rigorously.

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.

The project will benefit from the laboratory’s previous work in human stress, motivation, immersive behavioral testing and computational analysis, as well as from interactions with VR, engineering and data-analysis support structures at EPFL and in the Lausanne/Geneva area.

This position offers the opportunity to contribute to a methodologically innovative project at the interface of neuroscience, engineering, psychophysiology, human behavior and computational analysis. The project is expected to generate high-impact scientific publications and validated behavioral tools that can later be applied in both basic and clinical research.

Employment conditions

Institution: EPFL, Lausanne, Switzerland

Laboratory: Laboratory of Behavioral Genetic

Funding: Swiss National Science Foundation

Position type: Postdoctoral researcher

Duration: Three years of SNSF-funded project support; contracts are issued for one year and renewable annually according to standard EPFL procedures

Workplace: Lausanne, Switzerland, with interactions involving EPFL and relevant VR facilities in the Lausanne/Geneva area

EPFL offers outstanding international research environment, state-of-the-art facilities and a vibrant scientific community. We are committed to fostering diversity, equity and inclusion, and encourage applications from candidates of all backgrounds.

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 but encouraged: links to code repositories, analysis pipelines, experimental software, technical projects or other outputs illustrating the candidate’s expertise.

Applications will be reviewed on a rolling basis until the position is filled. The expected starting date is flexible and can be discussed. For any further information, please contact Prof. Carmen Sandi ([email protected]).

Contract Start Date : August 1st, 2026

Activity Rate : 100.00

Contract Type: CDD

Duration: 1 year, renewable

Reference: 2230

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