PhD scholarship in Uncertainty Quantification for Deep Learning - DTU Compute

Do you want to a career in machine learning research, and are you motivated to work on uncertainty quantification? DTU Compute invites applications for a 3-year PhD position.
Do you want to do research on cutting edge machine learning methods?

If you are establishing a career as a researcher in machine learning, and you are motivated to work with the latest methods for quantifying uncertainty in neural networks, we can offer you the best possible foundation. We seek a highly motivated and talented PhD student to join our group at DTU Compute, and we offer a funded PhD scholarship (3 years employment) in a vibrant interdisciplinary research environment.

The position is part of a larger research project, "Bayesian neural networks for molecular discovery", and you will join an enthusiastic team working towards the goal of 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 will 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 stochastic Markov chain Monte Carlo (MCMC) sampling.

Responsibilities
During the PhD program, you are expected to:

• Develop novel methods for uncertainty quantification in deep learning.
• Work with state-of-the-art nerual 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.
• Assist in machine learning teaching and supervision.

Qualifications
Candidates should have the following required skills:

• Proven experience in Bayesian methods, probabilistic modeling, and probability theory.
• A strong grasp of the theoretical foundations and practical implementation of Markov chain Monte Carlo (MCMC) methods.
• Proven experience with implementing machine learning methods in Python and Pytorch/Tensorflow.
• High level of motivation and creative problem solving skills.
• Excellent communication and writing skills in English.

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.

Assessment
The assessment of the applicants will be made by Associate Professor Mikkel N. Schmidt.

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 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 Mikkel N. Schmidt, [email protected] / www.mikkelschmidt.dk.

You can read more about DTU Compute at www.compute.dtu.dk/english.

If you are applying from abroad, you may find useful information on working in Denmark and at DTU at DTU – Moving to Denmark. Furthermore, you have the option of joining our monthly free seminar “PhD relocation to Denmark and startup “Zoom” seminar” 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 12 May 2024 (23:59 Danish time). Interviews are held continuously.

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
• Grade transcripts and BSc/MSc diploma (in English) including official description of grading scale

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, race, disability, religion or ethnic background are encouraged to apply.

DTU Compute
DTU Compute is a unique and internationally recognized academic department with 385 employees and 11 research sections spanning the science disciplines mathematics, statistics, computer science, and engineering. We conduct research, teaching and innovation of high international standard – producing new knowledge and technology-based solutions to societal challenges. We have a long-term involvement in applied and interdisciplinary research, big data and data science, artificial intelligence (AI), internet of things (IoT), smart and secure societies, smart manufacturing, and life science. At DTU Compute we believe in a diverse workplace with a flexible 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.

Adresse:

Matematiktorvet
2800 Kgs. Lyngby