Last Date: 05/01/2024.
JOB DESCRIPTION
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.
Responsibilities and qualifications
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 ideal candidate is expected to have a very strong background in machine learning and must have a PhD in machine learning (or a closely related topic). Furthermore, the candidate must have experience with research on deep generative models. Previous postdoctoral experience in machine learning and international experience will be considered an advantage. Proficiency in programming with Python/PyTorch/TensorFlow/Jax and a good command of the English language are essential.
As a formal qualification, you must hold a PhD degree (or equivalent).
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 terms of employment
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 2 years. The starting date is the 1 August 2024 (or according to mutual agreement). The position is a full-time position.
You can read more about career paths at DTU here.
Further information
Further information may be obtained from Associate Professor Jes Frellsen ([email protected]).
You can read more about DTU Compute at 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.
Application procedure
Your complete online application must be submitted no later than 30 April 2024 (23:59 Danish time). Interviews may be conducted rolling. 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:
- Application (cover letter)
- CV
- Academic Diplomas (MSc/PhD – in English)
- List of publications
- Optional: letters of recommendation
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.
