Jobs

We invite applications for two distinct two-year postdoctoral positions that promise a platform for professional growth and the prospect of contributing to cutting-edge research with substantial real-world impact. These positions are embedded within collaborative research initiatives dedicated to advancing and applying advanced generative modelling techniques, including diffusion and score-based modelling, aimed at addressing complex challenges in the respective fields of physical systems and life science.

The first position focuses on integrating physics-aware machine learning to enhance the computational efficiency of simulations critical for understanding natural phenomena and advancing technologies for environmental sustainability. To allow larger and more complex simulations of physical systems, we wish to do simulations where the physics only has been solved in a few discrete points and then use deep generative models to, e.g., fill in the simulation in the remaining space in a physically correct way. We want to initially attempt this for magnetic fields because magnets are used in many sustainable applications, and their physics is well-described and understood. This role will work initially on learning deep generative models to interpolate and extrapolate in a physically correct way by training it not only on magnetic field simulations but also defining the model such that Maxwell’s equations governing the physics of magnetic fields are fulfilled. This technique can then be transferred to additional physics subsequently by defining similar appropriate models for these. The postdoc will be co-supervised by Professor Rasmus Bjørk from DTU Energy.

The second postdoc position will be affiliated with the Centre for Basic Machine Learning in Life Science (MLLS, https://mlls.dk), a centre established to develop a solid machine learning foundation for research in the life sciences. This role focuses on methodological developments of deep generative models, particularly diffusion and score-based models, with potential applications in any branch of the life sciences. In the centre, we are particularly interested in representation learning and in settings where data is noisy and incomplete. The position can be co-supervised by any other PI from MLLS.

Both positions are methodologically oriented and prioritize the advancement of deep generative models.

The National Quantum Algorithm Academy (NQAA) under DeiC is offering 2 or 3 years fully funded Postdoc scholarships in the areas of Quantum Algorithms or Quantum- Software. The Postdoc project shall develop, study or test quantum algorithms, related software and their applications. The scholarship is required to begin in 2024 and is conditioned on the successful Postdoc applicant possessing the PhD degree not older than five years before commencing the scholarship. Exceptions, such as maternity leave of military service, may be accepted if motivated and documented in the application. Scholarships can be awarded to perform research within the traditional STEM fields or within other fields such as health science, social sciences, and humanities. Interdisciplinary projects are welcome.

We invite applications for a position as Tenure Track Assistant Professor or Associate Professor in Visual Computing, Computer Graphics and Animation. The position is to be filled by 1 November 2024 or as soon as possible thereafter, subject to negotiation.

We are looking for an innovative researcher with intellectual curiosity to strengthen and complement the research profile of the department within Visual Computing or Computer Graphics and Animation. Research areas of interest concerns computer animation, computer graphics, and geometry processing in a broad sense. This includes but is not limited to data-driven character animation, neural geometry processing, inverse rendering, and real-time rendering. Candidates are expected to publish in premier venues within Computer Graphics including but not limited to SIGGRAPH, SCA, EG, ACM TOG, CGF, IEEE TVCG, CHI, and GDC.

The position offers opportunities to teach and build a research team in a thriving environment with strong connections to Danish computer gaming industry, robotics and research and applications in Extended Reality (XR). A priori, the researcher will join the IMAGE research section, but the section affiliation will be negotiated in the hiring process.

As machine learning algorithms become increasingly prevalent in everyday life, needing substantial volumes of user data, it is critical to consider their societal impacts. This project aims to enhance the trustworthiness—encompassing privacy, fairness, and robustness—of contemporary machine learning algorithms, particularly when faced with inadequate data (e.g., noisy, unlabelled, biased, etc.). Consequently, this project will offer an opportunity to delve into a wide array of topics within modern machine learning, including differential privacy, semi- and self-supervised learning, robust machine learning techniques, and ensuring fairness within machine learning models.

We are seeking exceptional candidates with expertise in Machine Learning and Statistics. Ideal applicants may come from a diverse range of areas within machine learning, such as learning theory, robust and private machine learning, as well as semi-supervised or self-supervised learning approaches. This project encompasses both theoretical and applied sides, inviting applicants with a keen interest in either domain to apply. Additionally, individuals with a background in theoretical aspects of Computer Science or Mathematics, who have prior experience with machine learning, are encouraged to join our team.
The project will be supervised by Professor Amir Yehudayoff (amye@di.ku.dk) and Assistant Professor Amartya Sanyal (amsa@di.ku.dk) in the University of Copenhagen and in collaboration with Professor Varun Kanade (varun@cs.ox.ac.uk) at the University of Oxford (including a research stay at Oxford).  

Do you want to advance the state-of-the-art research in computer vision for material and computational design? 

The Technical University of Denmark (DTU) invites applicants for a postdoctoral researcher. The position is offered at DTU, in the Department of Computer Science and Applied Mathematics, at the Visual Computing Group, and will work closely with researchers from the Royal Danish Academy. The position is part of the research project Matters which is funded by a Villum Synergy grant. Matters is focused on material and computational design for analyzing, characterizing and developing novel wood composite structures. 

As part of the project, we offer two postdoc positions. The two postdocs will work closely together with the two project PI’s, Professor MSO Isak Worre Foged (The Royal Danish Academy) and Associate Professor Dimitrios Papadopoulos (Technical University of Denmark). One postdoc is formally employed at the Royal Academy with the candidate expected to have a background in architecture, design, or engineering with previous studies in material and computational design. One postdoc is employed at the Technical University with the candidate expected to have a background in computer science with previous studies in computer vision and machine learning.
This project will be supervised by Associate Professor Dimitrios Papadopoulos and Professor MSO Isak Worre Foged. 

Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The project is supported by a grant by the Independent Research Fund of Denmark for conducting basic research in Deep Learning-based financial investor network analysis. Work will be focused on proposing new techniques and methodologies, including graph neural networks, convolutional neural networks, and spatio-temporal deep learning models, for financial graphs coming from stock exchanges to detect investment activities, analysis of changes in investor activities over time, and investor community detection. The popularized description of the project is as follows:

The inability of conventional financial models to meet empirical observations from real-world markets has led to a growing level of dissatisfaction. Existing models set unrealistic assumptions for investors’ behavioural characteristics and the information flow inside and outside the market. The few recent interdisciplinary studies following data-driven analyses are based on crude behavioural investor categorizations, generic attributes for expressing properties of these investor categories (like age and gender), and simple (linear) mathematical models.

We invite applications for a PhD position financed by the Horizon Europe project PANDORA and the NordForsk project Nordic University Cooperation on Edge Intelligence (NUEI). The selected candidate will have good opportunities to work, interact, and network with researchers, scientists, and engineers working in European Universities, Research Institutes, and Companies forming its consortium. In addition, the PhD candidate has opportunities to participate in the summer schools and PhD courses in NUEI.

The PhD project aims at developing novel techniques for distributed learning and inference for Transformer-based Deep Learning models in the IoT-Edge-Cloud continuum. Large DNN models with enormous number of model parameters hinder their deployment on a single resource-constrained device. The project aims to design novel learning and inference algorithms to accelerate distributed learning and inference processes, for example, how to leverage model sparsity to reduce inference time and memory footprint, and how to leverage parallelism. In addition, from system perspective the project aims to efficiently manage the trade-off between resource utilisation, and inference accuracy, communication and computation time and energy consumption across the continuum, by adapting to the dynamic changes in the communication networks and data processing environment. We will propose and develop novel algorithms for dynamic workload distribution, load balancing, and energy management, to optimise distributed learning and inferencing in the IoT-Edge-Cloud continuum. The research outcomes will contribute to the advancement of resource-constraint foundation models.

As part of the PhD project, the selected candidate will interact with the collaborators in the two projects, perform knowledge dissemination, e.g., through conference participation and project reporting to present their work, and have the opportunity to participate in activities of the project.

The Novo Nordisk Foundation Centre for Biosustainability (DTU Biosustain) along with the Department of Applied Mathematics and Computer Science (DTU Compute) and Department of Biotechnology and Biomedicine (DTU Bioengineering) at the Technical University of Denmark are opening a Ph.D. position.
If your expertise lies in machine and deep learning, and you are looking to make a real-world sustainability impact through biotechnology, this is your chance to contribute. Your efforts will play a vital role in driving the sustainability performance of bio-based solutions for natural products, microbial foods, and sustainable chemicals by applying in-silico and human-in-the-loop strategies for modeling. 

We are actively seeking motivated Ph.D. candidates with skills in machine learning, deep learning, programming, and big data, with an interest biotechnology—a skill set in high demand in industry and academia. To bridge this gap, we offer a biosustainability-focused Ph.D. project supervised by Line Katrine Harder Clemmensen (DTU Compute) with co-supervision from Sumesh Sukumara (DTU Biosustain) and Marjan Mansourvar (DTU Bioengineering).