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.

Constructor University in collaboration with Constructor Knowledge Labs and Constructor Technology

About the Position

The research group led by Prof. Dr. Andrey Ustyuzhanin at Constructor University, in collaboration with Constructor Knowledge Labs (CKL) and Constructor Technology (industry partner), invites applications for Ph.D. student positions in the field of Computer Science, with a focus on Artificial Intelligence (AI) and Machine Learning (ML).

This PhD position is part of an initiative to advance knowledge representation and adaptive reasoning systems. The research will focus on developing flexible frameworks for actionable knowledge representation that support storage, retrieval, and dynamic adaptation of information across diverse tasks.

Key objectives include:

• Transforming unstructured data into interactive knowledge graphs and personalized cognitive maps.
• Designing models that provide interpretable, persistent, and navigable structures of knowledge.
• Addressing challenges such as hierarchy, composability, and coarse-graining for robust, task-specific reasoning.
• Exploring individual and community-level knowledge modeling, including personalized domain maps, profile extraction from artifacts (e.g., papers, courses), and cross-domain abstraction.

The overarching goal is to create systems that enable transparent, adaptive, and spatially intuitive representations of knowledge, supporting both individual users and collaborative communities.

Applicant Profile

Mandatory requirements:

• Holding recognized MSc degree (or equivalent) in Computer Science, AI, ML, or a related discipline.
• Students holding BSc degree and exhibiting outstanding performance and extraordinary potential can apply for fast-track PhD.
• Strong mathematical background supported with experience in defining and developing knowledge-graph or information retrieval systems.
• Hands-on experience with large language models (LLMs) and their applications.
• A track record of publications in AI/ML or related areas.
• Documented experience in practical research work.
• Strong skills in academic English writing (peer-reviewed papers, reports, or equivalent).

Funding & Appointment Terms

The appointment provides full financial coverage through a dedicated fellowship, comprising:

• Monthly stipend of €1,650
• Monthly research-cost allowance of €100 (Forschungskostenpauschale)
• Health-insurance subsidy of €100 per month
• Supplementary €550 mini-job allowance to support parallel part-time employment (optional)

Application Details

• Expected start date: March, 2026

Application package must include: 

• Curriculum Vitae (CV);
• Academic transcripts ;
• 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.

About the position

You are expected to contribute to the development of internationally visible, foundational research in AI-driven semantic structure extraction, automated reasoning-flow modeling, and adaptive content generation. The research focuses on methods for analyzing and representing deep semantic and pedagogical structures in scientific and educational materials; high-fidelity extraction of conceptual and reasoning blocks; inference-time rationale generation; and adaptive, learner-aware sequencing of content. This includes work on semantic parsing, structured NLP, graph-based neural models, metacognitive prompting, ontology alignment across disciplines, and human-in-the-loop optimization.

In this context, interdisciplinary research is strongly encouraged—particularly collaborations spanning computer science, computational linguistics, cognitive science, and the learning sciences. You will contribute to developing datasets, baseline models, personalized learning engines, reasoning-graph representations, cross-domain mapping algorithms, and RLHF-style feedback loops that improve system interpretability and instructional quality. The successful candidate will also contribute to high-quality publications, release research prototypes, and support demonstrator systems that deliver structured semantic extraction, rationale-aware content generation, and cross-domain transfer of reasoning structures.

Additionally, the successful candidate is expected to support teaching activities in areas such as Machine Learning, Natural Language Processing, AI in Education, Knowledge Representation, and Python-based analytical seminars at the BSc, MSc, and PhD levels. Responsibilities include assisting in course delivery, advising students, supervising Bachelor/Master theses, and engaging in methodological innovation for online, hybrid, and in-person learning environments. The university provides strong support for early-career researchers, including mentorship, administrative assistance, access to computational resources, conference funding, and opportunities to collaborate with other research groups and industrial partners working at the intersection of AI and digital education.

Mandatory requirements

• Master’s or PhD degree in Computer Science, Artificial Intelligence, Machine Learning, Computational Linguistics, or a related field.
• Strong research interest and practical experience in one or more of the following areas:
  • semantic parsing and structured representation learning
  • knowledge graphs or graph-based reasoning
  • transformer models, sequence modeling, or GNNs
  • natural language generation and explainability
  • educational AI, personalization algorithms, or cognitive modeling
• Evidence of research potential through publications, a strong thesis, or significant projects.
• Experience or interest in innovative teaching and learning approaches.
• Ability to translate theoretical insights into engineered prototypes and systems supporting scientific or educational use cases.
• Responsible, self-motivated, and capable of working independently as well as collaboratively.
• Excellent verbal and written communication skills.
• Intercultural competence and experience in international environments.
• Fluency with co-pilot tools for coding and writing.
• Fluency in English, the language of instruction and communication on campus.

Funding & Appointment Terms

The appointment provides full financial coverage through a dedicated fellowship, comprising:

• Monthly stipend of €1,650
• Monthly research-cost allowance of €100 (Forschungskostenpauschale)
• Health-insurance subsidy of €100 per month
• Supplementary €550 mini-job allowance to support parallel part-time employment (optional)

Application Details

• Expected start date: March, 2026

Application package must include: 

• Curriculum Vitae (CV);
• Academic transcripts ;
• 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.

Constructor University Bremen gGmbH and Constructor Knowledge Lab invite applications for PhD positions in Information Systems and Management Studies. The position will be supervised by Professor Isak Frumin (LinkedIn Profile).

This position focuses on understanding how universities worldwide are developing and implementing innovative institutional models, governance structures, and teaching practices in response to global social, technological, and economic changes. It offers the opportunity to engage in comparative research, join an international expert community, and contribute to publications and knowledge-exchange projects on institutional innovation and strategic management in higher education.

Research focus may include topics such as:

• Emerging models of innovative universities and their governance forms.
• Institutional reform, interdisciplinarity, and new models of research collaboration.
• Strategies for university transformation in knowledge-intensive and technology-driven environments.
• International comparisons of higher education innovations and their diffusion patterns.
• Knowledge management and organizational learning in research and education institutions.
• Emerging global trends in university-industry collaboration and digital ecosystems.

The project combines organizational theory, information systems, and higher education studies, allowing the candidate to address both theoretical and applied dimensions of institutional innovation.

Mandatory requirements:

• Master’s degree (or equivalent) in information systems, management, economics, sociology, political science, or higher education studies.
• Strong analytical and academic writing skills in English (demonstrated through thesis or publications).
• Demonstrated interest in organizational transformation, technology adoption, or data analytics in education or research contexts.
• Research experience using qualitative, quantitative, or mixed methods.

Preferred qualifications:

• Practical or academic background in data-driven analysis, management science, or organizational research.
• Some proficiency in German (speaking or writing) is desirable but not mandatory.

Funding & Appointment Terms

This is a self-funded PhD position. Constructor University subsidizes administrative fees for accepted candidates and provides access to its research infrastructure, office space, and supervision network. In addition, Constructor University Bremen gGmbH actively supports candidates in preparing applications for external funding — for example, doctoral scholarships, foundations, or international mobility grants, and can provide institutional support and references for such applications.

Application Details

Starting date: Fall 2026.

Required documents:

• Cover letter (1–2 pages) explaining your motivation, research interests, and fit with the project.
• CV (including publications or research projects).
• Master’s degree certificate and transcripts.
• Contact information for 2–3 possible academic referees.
• English proficiency certificate (if applicable).
• Optional: sample of research writing (e.g., thesis or publication).

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

Constructor University in collaboration with Constructor Knowledge Labs and Constructor Technology

About the Position

Constructor University, in collaboration with Constructor Knowledge Labs (CKL) and Constructor Technology (industry partner), invites applicants holding MSc degree and with a very strong scientific profile for Ph.D. student positions in the field of Computer Science, with a focus on Artificial Intelligence (AI) and Machine Learning (ML).

In this position students will contribute to research projects in CKL and as part of their education, will also engage in a dedicated 6-months internship period at Constructor Technology (CT), gaining first-hand industrial experience.

Mandatory requirements:

• Holding recognized MSc degree (or equivalent) in Computer Science, AI, ML, or a related discipline;
• Hands-on experience with large language models (LLMs) and their applications;
• A track record of publications in AI/ML or related areas;
• Strong skills in academic English writing (peer-reviewed papers, reports, or equivalent);
• Hands-on experience with research, demonstrated through publications and/or open-source contributions;
• Depending on the project strong mathematical background supported with experience in defining and developing knowledge-graph or information retrieval systems.

Funding & Appointment Terms

The appointment provides full financial coverage through a dedicated fellowship, comprising:

• Monthly stipend of €1,650
• Monthly research-cost allowance of €100 (Forschungskostenpauschale)
• Health-insurance subsidy of €100 per month
• Supplementary €550 mini-job allowance to support parallel part-time employment

This structure follows best practices of European research funding programs and ensures that the PI can pursue research objectives with both financial and administrative stability.

Application Details

• Applications will be reviewed on a rolling basis.

Applications should include:

• Curriculum Vitae (CV)
• Academic transcripts
• A detailed letter of motivation outlining research interests and career goals
• 2 letters of reference

Shortlisted candidates will be invited to interviews. Admission decisions will be announced by December 20, 2025.

Constructor University in collaboration with Constructor Knowledge Labs and Constructor Technology

About the Position

Constructor University, in collaboration with Constructor Knowledge Labs (CKL) and Constructor Technology (industry partner), invites applicants holding BSc degree and with a very strong scientific profile for fast-track Ph.D. student positions in the field of Computer Science, with a focus on Artificial Intelligence (AI) and Machine Learning (ML).

In this position students will contribute to research projects in CKL and as part of their education, will also engage in a dedicated 6-months internship period at Constructor Technology (CT), gaining first-hand industrial experience.

Mandatory requirements:

• Students holding BSc degree in Computer Science, AI, ML, or a related discipline and exhibiting outstanding performance and extraordinary potential can apply for fast-track PhD.
• Hands-on experience with large language models (LLMs) and their applications.
• A track record of publications in AI/ML or related areas.
• Strong skills in academic English writing (peer-reviewed papers, reports, or equivalent).
• Strong mathematical background supported with experience in defining and developing knowledge-graph or information retrieval systems, strong programming skills.

Funding & Appointment Terms

The appointment provides full financial coverage through a dedicated fellowship, comprising:

• Monthly stipend of €1,650 
• Monthly research-cost allowance of €100 (Forschungskostenpauschale)
• Health-insurance subsidy of €100 per month
• Supplementary €550 mini-job allowance to support parallel part-time employment

This structure follows best practices of European research funding programs and ensures that the PI can pursue research objectives with both financial and administrative stability.

Application Details

• Applications are reviewed on rolling basis.

Applications should include:

• Curriculum Vitae (CV)
• Academic transcripts
• A detailed letter of motivation outlining research interests and career goals
• 2 letters of reference
• Link to GitHub profile

Shortlisted candidates will be invited to interviews. Admission decisions will be announced by 10 April 2026.

Constructor Knowledge Labs (CKL), Bremen, Germany

In collaboration with Constructor University and Constructor Technology

Duration: Flexible; a summer start is expected, with the possibility of an earlier start by mutual agreement.

About the Position

Constructor Knowledge Labs (CKL) invites applications for a research internship embedded within the Human-AI Fusion & Cognitive Wearable Intelligence project. This project explores computational modeling of multimodal physiological and behavioral data, integration of cognitive and affective state inference with wearable technologies, and foundational aspects of adaptive human-AI collaboration.

The internship supports ongoing research by assisting in data processing, experiment preparation, model analysis, and evaluation workflows that underpin cognitive inference models and multimodal data fusion. Work will be supervised by senior researchers involved in interdisciplinary research spanning cognitive neuroscience, computational modeling, and AI-driven data analysis.

The role is designed to provide exposure to experimental design, multimodal data integration, and prototype analysis within an active scientific research environment.

Main responsibilities:

• Assist in the preparation and preprocessing of physiological, behavioral, and contextual data collected from wearable devices for research analysis.
• Support exploratory data analysis and basic modelling tasks to link multimodal signals with cognitive or affective state indicators.
• Help in setting up experimental protocols, data collection pipelines, and quality control procedures under guidance.
• Contribute to implementation and evaluation of basic analytical pipelines or prototype components (e.g., signals processing, feature extraction scripts).
• Conduct literature reviews on relevant research themes such as multimodal data fusion, cognitive modeling, or wearable AI inference.
• Document methods, preliminary results, and routine analyses in internal notes or reports.

Requirements:

• Currently enrolled in a Bachelor’s or Master’s programme in Cognitive Science, Psychology, Computer Science, Artificial Intelligence, Data Science, or a closely related field.
• Basic programming skills (e.g., Python) and familiarity with data processing workflows.
• General awareness of machine learning, statistical analysis, or signal processing concepts.
• Interest in multimodal data analysis, wearable technologies, or human-AI interaction.
• Strong analytical thinking and ability to engage with interdisciplinary research tasks.
• Good written and spoken English.

We offer

• Research internship with flexible duration in an international, interdisciplinary research environment.
• Participation in active research projects at the intersection of AI, knowledge systems, and applied research, with a clearly scoped internship-level contribution.
• Close supervision and mentoring by leading PIs and senior researchers, whose work shapes current research agendas and attracts significant scholarly attention and citations.
• Opportunity to contribute to academic publications, embedded in established research groups with consistent presence at top-tier conferences and journals.
• Flexible working arrangements, including hybrid or remote options.

Application details

Please submit the following documents in English:

• CV
• Short motivation letter (max. 1 page) explaining your interest and relevant skills
• Transcript of records (GPA obligatory)

Deadline for full consideration: 20 March 2026, 23:59 CET.