PhD scholarship in Uncertainty Quantification for Large Language Models - D at Danmarks Tekniske Universitet

Copenhagen, Capital, Denmark -

Full Time

Start Date

Immediate

Expiry Date

17 Sep, 26

Salary

0.0

Posted On

19 Jun, 26

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Uncertainty Quantification, Large Language Models, Probabilistic Machine Learning, Bayesian Deep Learning, PyTorch, JAX, Fine-tuning LLMs, Academic Publishing, Approximate Bayesian Inference, Natural Language Processing

Industry

Research Services

Description

If you are passionate about machine learning and looking for an ambitious, interdisciplinary PhD project at the forefront of trustworthy AI, this position offers exactly that. You will develop principled methods for uncertainty quantification in large language models, work alongside leading researchers in Denmark and at the University of Edinburgh, and contribute to making one of today's most widely used technologies more reliable and trustworthy. Responsibilities and qualifications The PhD position focuses on developing principled methods for uncertainty quantification in large language models (LLMs), with the goal of making LLMs more trustworthy by enabling them to quantify the confidence behind the statements they generate. You will work to adjust the level of confidence that is communicated with the user's perceived confidence. The project will advance principled methods for uncertainty quantification, for instance based on approximate Bayesian inference, and scale these methods to modern-size LLMs. The ideal candidate will have a solid background in machine learning and a strong interest in this area. The project is jointly led by Dr. Jes Frellsen at the Technical University of Denmark and Dr. Christian Hardmeier at IT University of Copenhagen (ITU), in close collaboration with Prof. Hannah Rohde at the University of Edinburgh. An extended research stay at the University of Edinburgh, hosted by Prof. Ivan Titov, is an essential part of the project. Your primary tasks will include: * Developing methods for uncertainty quantification in LLMs and scaling them to large models. * Aligning expressed confidence with statistical uncertainty estimates. * Collaborating with interdisciplinary partners across DTU, ITU, and Edinburgh. * Publishing and presenting research findings at leading machine learning and NLP venues. The following qualifications are desirable: * Experience with probabilistic machine learning, Bayesian deep learning, and uncertainty quantification. * Good knowledge of machine learning frameworks such as PyTorch or JAX. * Experience of training, fine-tuning, and evaluating large language models. * Experience of academic publishing (e.g., as a conference paper author). You must have a two-year master's degree (120 ECTS points) or a similar degree with an academic level equivalent to a two-year master's degree. Approval and Enrolment The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see DTU's rules for the PhD education [https://www.dtu.dk/english/Education/PhD/Rules]. Assessment The assessment of the applicants will be made by Associate Professor Jes Frellsen (DTU Compute). We offer DTU is a leading technical university globally recognized for the excellence of its research, education, innovation and scientific advice. We offer a rewarding and challenging job in an international environment. We strive for academic excellence in an environment characterized by collegial respect and academic freedom tempered by responsibility. Salary and appointment terms The appointment will be based on the collective agreement with the Danish Confederation of Professional Associations. The allowance will be agreed upon with the relevant union. The period of employment is 3 years. Starting date is in the fall of 2026 or according to mutual agreement. The position is full-time. You can read more about career paths at DTU here [http://www.dtu.dk/english/about/job-and-career/working-at-dtu/career-paths]. Further information Further information may be obtained from Associate Professor Jes Frellsen [https://frellsen.org/] (DTU Compute, jefr@dtu.dk). You can read more about the Department of Applied Mathematics and Computer Science at www.compute.dtu.dk [https://www.compute.dtu.dk/]. If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark [https://www.dtu.dk/english/about/job-and-career/moving-to-denmark]. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar [https://www.dtu.dk/english/about/job-and-career/moving-to-denmark/before-arrival/phd-relocation-to-denmark]” for all questions regarding the practical matters of moving to Denmark and working as a PhD at DTU. Application procedure Your complete online application must be submitted no later than 7 August 2026 (23:59 Danish time). Applications must be submitted as one PDF file containing all materials to be given consideration. To apply, please open the link "Apply now", fill out the online application form, and attach all your materials in English in one PDF file. The file must include: * A letter motivating the application (cover letter) * Curriculum vitae (including GPA for both BSc and MSc) * Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale * Relevant scientific publications or/and master thesis written by the applicant (not required but recommended) You may apply prior to obtaining your master's degree but cannot begin before having received it. Applications received after the deadline will not be considered. All interested candidates irrespective of age, gender, disability, race, religion or ethnic background are encouraged to apply. As DTU works with research in critical technology, which is subject to special rules for security and export control, open-source background checks may be conducted on qualified candidates for the position. DTU Compute DTU Compute – Department of Applied Mathematics and Computer Science – is an internationally recognised academic environment with over 400 employees and 10 research sections. We broadly cover digital technologies within mathematics, data science, computer science, and computer engineering, including artificial intelligence (AI), machine learning, internet of things (IoT), chip design, cybersecurity, human-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis, design, and implementation of complex systems. We focus on ensuring that our research results contribute to creating a better society by supporting areas such as health, green transition, energy supply, and life science. We collaborate with universities, public and private organisations, and companies in Denmark and abroad, and through DTU’s startup ecosystem, we encourage innovation and entrepreneurship. We have a strong ethical, human, and sustainable approach that ensures integrity in our work. Therefore, we strive for and take responsibility for driving the democratisation of digital technologies, so that everyone has the opportunity to actively participate in the development, and we ensure a continued open, democratic, and inclusive society for the benefit of all. At DTU Compute, we value diversity, inclusion, and a flexible work-life balance. Read more about us at www.compute.dtu.dk [http://www.compute.dtu.dk]. DTU – For the benefit of society since 1829 DTU is one of Europe's leading elite technical universities. Through research and education at an international top level, we create solutions to the major societal challenges of our time and help secure Europe's global leadership in sustainable technological development. Since Hans Christian Ørsted founded DTU almost 200 years ago, our mission has remained the same: We develop and create value through the natural and technical sciences for the benefit of society. DTU has 13,800 students, 1,600 PhD students, and 6,500 employees. We work in an international environment and have an inclusive, stimulating, and informal work culture. DTU has campuses in all parts of Denmark and in Greenland and collaborates with the best universities around the world.

Responsibilities

Develop and scale principled methods for uncertainty quantification in large language models to improve AI trustworthiness. Collaborate with interdisciplinary partners across DTU, ITU, and the University of Edinburgh to publish research findings.