Postdoc in Machine Learning: Uncertainty Quantification in Graph Neural Networks - DTU Compute

Do you want to do research on cutting edge machine learning methods? 

If you are building a career as a researcher in machine learning and are passionate about working with cutting-edge methods for quantifying uncertainty in neural networks, we offer an excellent opportunity to advance your research. We are seeking a highly motivated and talented postdoctoral researcher to join our team at DTU Compute, offering a fully funded position within a dynamic and interdisciplinary research environment.

This position is part of the larger research project "Bayesian Neural Networks for Molecular Discovery," where you will collaborate with an enthusiastic team dedicated to developing effective methods for neural network-based molecular discovery.

Project description
One of the central challenges facing modern machine learning is to understand and quantify uncertainty to ensure that AI-driven solutions deliver accurate and trustworthy insights. In this project, our goal is to develop novel methods for uncertainty quantification in deep neural networks. In particular, we focus on graph neural networks applied to the problem of molecular discovery. We envision that your role could be focused on developing and scaling techniques such as Bayesian neural networks or diffusion-based generative models, but the specific focus will depend on your research interests.

Responsibilities
You are expected to:

• Develop novel methods for uncertainty quantification in deep learning.
• Work with state-of-the-art neural network architectures applied to molecular data.
• Publish scientific papers and present research results in top machine learning conferences such as NeurIPS, ICML, UAI, and AISTATS. 
• Contribute to supervision and management within the research project.

Qualifications
Candidates should have the following required skills:

• Proven experience in Bayesian methods, probabilistic modeling, and probability theory.
• Proven experience with implementing machine learning methods in Python and Pytorch/Tensorflow. 
• A strong publication record within uncertainty quantification, Bayesian neural networks, graph neural networks, machine learning-based molecular discovery, diffusion-based generative models, or other related fields.
• High level of motivation and creative problem solving skills. 
• Excellent communication and writing skills in English. 

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. Starting date is 1 April 2025 (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 Mikkel N. Schmidt (mnsc@dtu.dk).

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 10 January 2025 (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:

• Application cover letter:
  • Personal motivation: Outline your reasons for applying and interest in the position.
  • Research statement: Provide an overview of the research directions you aim to explore.
  • Key publications: Briefly describe 1-3 of your most relevant works, such as journal/conference publications or your PhD thesis.
• CV
• Academic Diplomas (MSc/PhD – in English)
• List of publications

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 er et unikt og internationalt anerkendt akademisk institut med 385 ansatte og 11 forskningssektioner, der spænder over videnskabsdisciplinerne matematik, statistik, datalogi og ingeniørvidenskab. Vi udfører forskning, undervisning og innovation af høj international standard - der producerer ny viden og teknologibaserede løsninger til samfundets udfordringer. Vi har en mangeårig involvering i anvendt og tværfaglig forskning, big data og datavidenskab, kunstig intelligens (AI), internet of things (IoT), smarte og sikre samfund, smart fremstilling og life science. På DTU Compute tror vi på en mangfoldig arbejdsplads med en fleksibel work-life balance.

Technology for people
DTU develops technology for people. With our international elite research and study programmes, we are helping to create a better world and to solve the global challenges formulated in the UN’s 17 Sustainable Development Goals. Hans Christian Ørsted founded DTU in 1829 with a clear mission to develop and create value using science and engineering to benefit society. That mission lives on today. DTU has 13,500 students and 6,000 employees. We work in an international atmosphere and have an inclusive, evolving, and informal working environment. DTU has campuses in all parts of Denmark and in Greenland, and we collaborate with the best universities around the world.