PhD positions
Postdoc position
Swiss National Science Foundation (SNSF)-funded project on human motivation, stress physiology, immersive behavioral testing, and biological phenotyping
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 present call is for the position focused on human experimental implementation, participant recruitment, psychobiological assessment, biological sampling, and translational biomarker integration. A complementary position will focus more specifically on multimodal sensing, experimental systems, physiological and movement data, and advanced behavioral data analysis.
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 how stress influences motivated behavior requires experimental approaches that combine rigorous behavioral testing with careful assessment of individual differences and biological stress responses. In this project, 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 study will combine a standardized acute stress manipulation and behavioral assessments in VR, physiological monitoring, and biological sampling. In addition to endocrine measures, we are eventually interested -in the second part of the project implementation- in integrating broader hormonal, metabolic, inflammatory, and mitochondrial-related biomarkers to better characterize individual biological profiles linked to stress responsiveness and motivated behavior.
A central aim is to develop and validate novel motivational tasks for VR and to understand how anxiety-related traits, stress physiology, and biological response profiles shape motivated behavior. The successful candidate will play a key role in ensuring that the human experimental pipeline is scientifically rigorous, ethically sound, well organized, and suitable for future translational applications.
Main responsibilities
The postdoctoral researcher will lead the human experimental and translational phenotyping arm of the project. Responsibilities will include:
-
- Coordinating participant recruitment, screening, scheduling, consent, testing, and retention.
- Implementing the human experimental protocol with high fidelity, including acute stress and neutral control sessions, VR-based behavioral tasks, questionnaires, physiological recordings, and biological sampling.
- Developing and maintaining standard operating procedures for participant flow, stress testing, biological sample collection, sample processing, data tracking, and quality control.
- Training and supervising Master’s students, research assistants, or junior lab members involved in recruitment, participant testing, biological sample handling, and data entry.
- Overseeing saliva collection, processing, labeling, storage, and coordination of endocrine assays, particularly cortisol.
- Contributing to the possible extension of the project toward blood-based hormonal, metabolic, inflammatory, or mitochondrial-related biomarkers.
- Ensuring high-quality participant-facing implementation, including participant safety, protocol adherence, management of pre-visit restrictions, timing of biological samples, and standardized administration of questionnaires and tasks.
- Working closely with the complementary postdoctoral researcher to align biological, physiological, behavioral, and movement-based data streams.
- Contributing to ethics submissions, amendments, participant-facing documents, laboratory manuals, data-management procedures, and open-science deliverables.
- Analyzing and interpreting relationships among stress exposure, anxiety-related traits, endocrine and physiological responses, and motivated behavior.
- Preparing reports as well as manuscripts for publication in top scientific journals and presenting findings at relevant conferences.
Candidate profile
Applicants should have a PhD in psychology, neuroscience, cognitive neuroscience, affective neuroscience, biological psychology, psychoneuroendocrinology, clinical neuroscience, translational psychiatry, human physiology, or a related discipline.
The ideal candidate will combine strong scientific understanding of human stress and motivation with excellent organizational skills and hands-on experience in participant-facing experimental research. We are looking for someone able to lead a complex human study with rigor, care, and independence, while also contributing intellectually to the interpretation of the biological and behavioral findings.
Essential qualifications include:
- Experience designing, coordinating, or running human experimental studies.
- Strong interest and expertise in stress, motivation, anxiety, affective neuroscience, reward, effort-based behavior, individual differences, or related domains.
- Experience with biological sampling in human participants, preferably saliva and/or blood.
- Familiarity with psychophysiological measures such as heart rate, heart-rate variability, electrodermal activity, respiration, or related indices.
- Knowledge of stress-related biomarkers, particularly cortisol, and ideally additional endocrine, metabolic, inflammatory, or mitochondrial-related measures.
- Experience with validated questionnaires and behavioral phenotyping in humans.
- Strong ability to manage participant recruitment, scheduling, screening, consent, protocol adherence, and study logistics.
- Ability to train and supervise students or research assistants involved in data collection.
- Good statistical literacy and ability to analyze behavioral, questionnaire, physiological, or biomarker data.
- Excellent attention to detail, documentation skills, and ability to maintain rigorous sample and data-tracking procedures.
- Strong scientific writing skills and motivation to contribute to high-quality publications.
Prior VR experience
Prior experience with virtual reality is welcome but not required. The successful candidate is not expected to be the main VR programmer or technical systems developer. However, they should be comfortable working in an immersive behavioral testing environment and ensuring that participant-facing procedures are implemented reliably and consistently.
Relevant experience may include human stress induction protocols, psychophysiology, behavioral testing, clinical or subclinical phenotyping, biological sample collection, experimental medicine studies, or translational human neuroscience.
Additional strengths
Additional assets include experience with acute stress protocols such as the Trier Social Stress Test or related paradigms; saliva cortisol sampling; blood collection and processing; Biopac or comparable physiological acquisition systems; ELISA or immunoassay coordination; hormonal assays; metabolic or inflammatory biomarkers; mitochondrial-related peripheral measures; biobanking procedures; ethics submissions; longitudinal or large-scale human studies; and reproducible data-management practices. Experience in stress research, motivation, affective neuroscience, computational psychiatry, neuroeconomics, or human decision-making would be valuable
French proficiency is a strong asset, as participant recruitment and testing will take place in the Lausanne/Geneva area. Excellent English communication and writing skills are required.
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 psychobiological phenotyping, as well as from interactions with VR, engineering, physiology, and data-analysis support structures at EPFL and in the Lausanne/Geneva area.
This position offers the opportunity to contribute to a methodologically innovative and translationally relevant project at the interface of human stress research, motivation, psychophysiology, biological sampling, and behavioral neuroscience. The project is expected to generate strong scientific publications and validated behavioral tools that can later be applied in both basic and clinical research.
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: examples of previous protocols, human-study coordination experience, biomarker work, analysis pipelines, or other outputs illustrating the candidate’s expertise.
Applications should be sent to:
Prof. Carmen Sandi
Laboratory of Behavioral Genetics
EPFL, Lausanne, Switzerland
Applications will start to be reviewed from June 15th 2026 and will continue being assessed on a rolling basis until the position is filled. The expected starting date is flexible starting from 1.08.2026 and can be discussed.
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 an 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.
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 present 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.
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 should be sent to:
Prof. Carmen Sandi
Laboratory of Behavioral Genetics
EPFL, Lausanne, Switzerland
Applications will be reviewed on a rolling basis until the position is filled. The expected starting date is flexible and can be discussed.
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 an 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.