Ph.D. positions in Computer Science for students holding MSc

Constructor Knowledge Labs

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.

Constructor Knowledge Labs

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.

Qube Research & Technologies

Eligible candidates: Final-year PhD students or postdoctoral researchers Qube Research & Technologies (QRT) is a global quantitative and systematic investment manager, operating in all liquid asset classes across the world. We are a technology and data driven group implementing a scientific approach to investing. Combining data, research, technology, and trading expertise has shaped our collaborative mindset which enables us to solve the most complex challenges. QRT’s culture of innovation continuously drives our ambition to deliver high quality returns for our investors.    Over the years, QRT has invested in a global research and execution platform which has been deployed to cover all geographies and asset classes. This platform covers a broad spectrum from high to low frequency trading systems. Our culture is centred around technology, automation, and industrialized processes. We operate in multiple languages from C++ to Python and embrace open-source software.     Your future role within QRT  You will join a team of quantitative researchers to learn how to design trading algorithms in a real-world data environment – a fast track to become a seasoned quantitative researcher. Surrounded by peers who have successfully transitioned from academia to applied research, you will receive mentorship from experienced professionals who will help you translate your theoretical expertise into real-world impact. The research environment at QRT is designed to be collaborative and intellectually stimulating.  Within one of QRT's systematic teams - spanning high, mid, and low-frequencies - your core objective will be to develop high-quality predictive signals:  * Leverage access to vast and diverse datasets to identify hidden statistical patterns and market opportunities.  * Collaborate with fellow researchers to exchange ideas and refine methodologies.  * Translate theoretical models into production-ready signals.  * Lead the full research cycle - from idea generation to implementation.    Your present skillset  * Holding or pursuing a PhD (final year) degree in a quantitative field such as statistics, mathematics, physics, biology, computer science, or engineering.  * A pragmatic attitude towards translating theoretical models into real-world data problems.    * Proficiency in Python (preferred) or another leading programming language such as R, MATLAB, C++, or C#.  * Experience working with large datasets across multiple time frames (a plus).  * Ability to multitask in a fast-paced environment with attention to detail.  * Intellectual curiosity to explore new data, solve complex problems, and connect ideas across disciplines.  * Ability to work autonomously, in a collegial and collaborative setting, and with colleagues from diverse backgrounds and areas of expertise.  * Strong communication skills.  * Fluency in English (additional languages are a plus).    Interviewing:    * Apply online: All applications are reviewed by our Talent Acquisition Team, on a rolling basis.  * Interviews: Conducted on-site or via Teams, they assess both your technical expertise and alignment with our collaborative culture.    We invite candidates for the Quantitative Research position to take part in one of our Data Challenges. This task is designed to give you insight into the daily responsibilities of the role and allows us to assess your abilities and interest: Challenge data (ens.fr)  QRT is an equal opportunity employer. We welcome diversity as essential to our success. QRT empowers employees to work openly and respectfully to achieve collective success. In addition to professional achievement, we are offering initiatives and programs to enable employees achieve a healthy work-life balance