About the position

You are expected to conduct and lead internationally competitive research in AI-driven semantic analysis and automated reasoning-flow modeling, with applications to educational materials, scientific documents, and research authoring. Your work will focus on developing advanced methods for semantic structure extraction, conceptual and argumentative flow reconstruction, rationale-aware content generation, metacognitive prompting, and adaptive personalization. Core research contributions will include designing algorithms for concept and structure extraction, building neural/graph hybrid models for pedagogical reasoning, implementing ontology-alignment methods for cross-domain transfer, and creating human-in-the-loop optimization pipelines that incorporate expert feedback—aligning with the multi-year research roadmap (WP1–WP3) .

As a postdoctoral researcher, you will take a leading role in designing experiments, shaping research directions, supervising junior researchers (including PhD students), and contributing to the development of end-to-end demonstrator systems capable of generating structured, rationale-rich, and learner-adaptive content. You will be responsible for coordinating data annotation efforts, overseeing the construction of evaluation frameworks, and driving publication-quality research outputs, including survey papers, methodology papers, and cross-domain transfer studies. Interdisciplinary collaboration is strongly encouraged, especially at the intersection of computer science, computational linguistics, cognitive science, and learning technologies, and you will have opportunities to initiate or co-lead joint projects with internal and external partners.

The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course development and supervising Bachelor’s, Master’s, and PhD theses. The university provides a supportive academic environment—including mentoring, administrative support, computational resources, and opportunities for technology transfer and industry engagement—enabling the postdoctoral researcher to establish or strengthen an internationally visible research profile.

Mandatory requirements

• PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a closely related field.
• Strong publication record in relevant top-tier venues (e.g., NeurIPS, ACL, ICML, ICLR, AIED, LAK, AAAI).
• Demonstrated expertise in at least two of the following areas:
  • semantic parsing or structured NLP
  • knowledge representation, reasoning graphs, or GNNs
  • natural language generation, explainability, or rationale modeling
  • ontology alignment, cross-domain transfer, or representation learning
  • personalization algorithms, metacognition, or educational AI
  • human-in-the-loop systems or RLHF
• Experience in leading research tasks, mentoring junior researchers, and coordinating multi-stage projects.
• Fluency in Python and modern deep learning frameworks (PyTorch/TensorFlow).
• Strong analytical, communication, and academic writing skills.
• Demonstrable ability to design rigorous experimental pipelines and validation methodologies.
• Responsible, independent, and proactive research personality with strong teamwork skills.
• Intercultural experience and readiness to work in an international research environment.
• Fluency with AI co-pilot assistants for coding and writing.
• Fluency in English, the primary language of research and instruction on campus.

Preferred qualifications:

• Experience in developing research prototypes, evaluation benchmarks, or large-scale datasets.
• Prior involvement in interdisciplinary or applied research projects.
• Potential to contribute to competitive funding applications.
• Experience with open-source releases or reproducible research pipelines.

Application Details

• Starting date: March, 2026
• Contract: Full-time (100%), TV-L E13
• Work mode: Hybrid

Application package must include: 

• Curriculum Vitae (CV);
• A detailed letter of motivation outlining research interests and career goals;
• 2 recommendation letters;

Applications to be reviewed on a rolling basis. Shortlisted candidates will be invited to interviews.

Position Overview

We are seeking a motivated early-career and visionary Principal Investigator (PI) to lead the neuroscience and cognitive modeling direction of the Human Ai fusion and cognitive.
The ideal candidate combines expertise in cognitive neuroscience, psychology, and computational modeling with strong understanding of AI-driven data analysis and practical experience with wearables.

Key Responsibilities

• Lead the research direction on Neuroscience and neuropsychology.
• Develop fusion data models linking wearable physiological and behavioral signals with cognitive and affective states.
• Design and conduct experimental studies (lab and field) to validate multimodal inference models, selecting and utilizing appropriate data sources such as EEG, heart rate, motion, and contextual signals.
• Collaborate on building the Agentic Personalization Engine (APE) that leverages cognitive modeling for adaptive, user-centered feedback.
• Guide integration of cognitive models into edge and wearable environments, defining trade-offs between accuracy, interpretability, and energy cost.
• Supervise postdocs, PhD students, and interns in interdisciplinary research combining neuroscience, machine learning, and data engineering.
• Coordinate with engineering and AI teams to ensure cognitive models are technically scalable and ethically compliant.
• Publish research results in top-tier venues and represent the project in academic and applied research communities.

Required Qualifications

• PhD in Cognitive Neuroscience, Experimental Psychology, Computational Cognitive Science, AI/ML, Data Science or a closely related discipline.
• Proven expertise in neurocognitive or behavioral data analysis, including physiological/multimodal data fusion.
• Solid understanding of machine learning, statistical modeling, and/or computational cognitive modeling.
• Track record of participating in interdisciplinary projects, preparing and submitting competitive grant applications that secured funding, and publishing in high-impact journals or conferences.
• Experience designing and executing human experiments and analyzing behavioral or physiological datasets.
• Strong ability to connect cognitive theory with computational methods.

Desirable Qualifications

• Experience with wearable sensing technologies (such as EEG headsets, smart rings, ECG, motion trackers, etc.).
• Background in Human-AI interaction or affective computing.

• Familiarity with edge AI or resource-constrained computation for real-time data processing.
• Experience building data fusion testbeds or benchmarking frameworks for cognitive or behavioral data.
• Knowledge of privacy-preserving ML, ethical constraints, dynamic consent management, and GDPR-compliant data workflows.
• Ability to collaborate across psychology, neuroscience, and AI engineering teams in both academic and applied contexts.

• Experience of leading research projects.

Project Description

This project explores how multimodal data fusion science (including wearable data streams) can be transformed into structured cognitive and behavioral insights and integrated into a knowledge-grounded digital twin (“Construct”) for bi-directional collaboration in education and research to increase the efficiency of work of scientists, students and teachers. The work centers on two complementary research directions:

1. Cognitive and Behavioral Data Fusion – Developing computational models and pipelines to integrate heterogeneous temporal data (physiological, behavioral, contextual, and cognitive), based on ground facts, collected about user into structured insight graphs. This enables a deeper understanding of user states — such as focus, engagement, and cognitive load — forming the foundation for adaptive, bi-directional context-aware personalization within the Agentic Personalization Engine (APE).
2. Human-AI Modeling & Wearable data processing– This includes exploring how compact AI models can run locally/on-edge devices to interpret user states and adapt recommendations in real time while preserving privacy and consent. 

The project will establish frameworks and benchmarks for cognitive-state modeling, privacy-preserving personalization, and ethically aligned human-AI collaboration.

Funding & Appointment Terms

Compensation will be determined based on the candidate’s profile, experience, and interview results.