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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Postdoctoral Researcher in Data Science and Artificial Intelligence - BISS - **Company:** Maastricht University - **Location:** Heerlen, Netherlands (Remote available) - **Salary:** €4,925 - €6,682 - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Big Data, Cognitive Science, Human-Computer Interaction, Machine Learning, Raw Data, Sensor Fusion, Signal Processing, Data Streaming, Virtual Reality, Deep Learning, Information Technology, Feature Extraction - **Published:** November 7, 2026 - **Apply:** https://www.academictransfer.com/en/jobs/365388/postdoctoral-researcher-in-data-science-and-artificial-intelligence-biss/apply/ ## About the Role We are interested in your expertise, scientific curiosity, and potential. You hold, or are close to completing, a PhD in data science, artificial intelligence, machine learning, computer science, computational cognitive science, biomedical engineering, cognitive science, mathematical psychology, or a related field. You have experience developing and evaluating machine-learning, statistical, or signal-processing methods for complex datasets, and you are comfortable working with different scientific programming environments. You are interested in combining multiple data streams, including (neuro)physiological, behavioral, and interaction signals. Experience with time-series analysis, physiological signal processing, sensor fusion, deep learning, representation learning, or related approaches is particularly welcome. We are looking for someone who enjoys building sophisticated models, but who also wants to understand why they work, what information they use, and how they relate to underlying psychological and physiological processes. An interest in virtual reality, adaptive systems, human-computer interaction, brain-computer interfaces, or physiological computing is therefore highly relevant. We are particularly interested in candidates who: * combine strong technical and quantitative skills with scientific curiosity about human cognition and physiology; * are proactive and independent, while enjoying interdisciplinary collaboration; * have a research track record appropriate to career stage and the ambition to publish in relevant scientific outlets; * communicate fluently in written and spoken English. Knowledge of Dutch is welcome, but not required. ## Description Are you excited about developing AI methods that can make sense of complex human signals? Do you have a strong interest in machine learning, multimodal data, (neuro)physiological sensing, and adaptive systems? Are you fascinated by building sophisticated predictive models, but equally interested in understanding the mechanisms and human processes behind them? Then this postdoctoral position may be the one for you., We are looking for a postdoctoral fellow with a strong academic interest in data science, machine learning, artificial intelligence, and multimodal human sensing. The successful candidate will work on the three-year VR-STRESS project. VR-STRESS is a cross-border research and innovation project aimed at developing an adaptive virtual reality system for medical training. The project investigates how (neuro)physiological, behavioral, and interaction data can be used to infer a user's cognitive and emotional state, and how these estimates can be translated into real-time adaptations of a virtual training environment. A central component of the project is the development of an integrated system that combines multiple non-invasive sensing modalities and transforms raw data into meaningful indicators of stress, cognitive load, attention, fatigue, emotional state, and performance. A key scientific challenge is to combine heterogeneous physiological, neurophysiological, and behavioral signals into robust and interpretable models that work across individuals and changing conditions. What you do You will take a leading role in the data science and artificial intelligence component of the VR-STRESS project. You will develop methods for processing, integrating, and interpreting multimodal physiological and behavioral data collected in controlled studies in which participants are exposed to different levels of stress, cognitive demand, and task complexity. Using signal processing, machine learning, and multimodal data-fusion techniques, you will investigate how patterns across different measurement channels can be used to estimate relevant aspects of user state. A central scientific question is not only how accurately these states can be predicted, but also what the models can tell us about the signals and mechanisms underlying them. You will therefore study model interpretability, robustness, individual variability, and generalizability, and validate model outputs against subjective self-reports, experimental manipulations, behavioral measures, and objective performance indicators. You will also contribute to the development of real-time or near-real-time methods that connect inferred user states to adaptive mechanisms in the VR environment. These mechanisms may, for example, dynamically adjust the intensity of stressors, task complexity, or the presentation of supportive information. Your work will focus on: * developing signal-processing, machine-learning, and multimodal data-fusion methods for heterogeneous human-sensing data; * building and evaluating robust and interpretable models of cognitive, emotional, and physiological user states; * translating these models into real-time or near-real-time algorithms for adaptive VR applications; * collaborating closely with experimental psychologists, cognitive scientists, engineers, and VR developers, and disseminating results through scientific publications and conferences.