Postdoc in Machine Learning using Tensor Networks (DTU)

Postdoc in Machine Learning using Tensor Networks - DTU Compute Are you interested in developing novel machine learning methodologies that are scalable, reliable and explainable and that can address imminent challenges both within quantum physics and the life-sciences? As our new colleague your job will be to develop novel computational frameworks for machine learning by use of tensor network representations. In particular, you will push the boundaries of Scalability, drawing upon recent large-scale TN capabilities in physics. Reliability, exploring uncertainty quantification and robust inference in machine learning Explainability, leveraging identifiability and unique recovery explored using tensor decompositions within the life-science domain. A Tensor Network (TN) is a data structure for representing high-dimensional arrays (tensors) in a low-rank format as a sequence of smaller cores which can be stored and manipulated efficiently. TNs provide promising tools for large-scale machine learning, as they allow for exponential savings in memory and processing time, while often allowing for explainable structure extraction. However, key obstacles remain, preventing their widespread use. These include limitations in terms of reliable large-scale inference, uncertainty quantification, as well as efficient TN structure identification, assessment, and interpretation. You will address these obstacles and demonstrate how the developed tools can address important challenges within quantum physics as well as large-scale life-science data modeling. Responsibilities and qualifications You are fascinated by machine learning and how computers can learn from data and you have a strong interest in the mathematical foundation of machine learning models. In this position, you will be responsible for developing novel tensor network based machine learning methodologies enabling new approaches for solving supervised, unsupervised and reinforcement learning problems. You will thereby leverage tensor networks as a computationally efficient and expressive framework that provides a complimentary generic framework to deep learning. Specifically, we are looking for a profile with the motivation and drive needed for making a difference that matters. You must bring an open mindset and like to create results via collaboration with multiple people with different professional and cultural backgrounds. You are a talented, self-motivated, and team-oriented person who enjoys working on the theoretical foundation of machine learning. In particular, your CV comprises: A strong relevant background within machine learning and mathematics. Extensive experience programming in Python (or Matlab). An active interest or experience in strong collaborations and interdisciplinary work at the intersection between machine learning, physics and life-science data modeling. You must be fluent in English, both speaking and writing and possess excellent communication skills. You will be supervised by Professor Morten Mørup at DTU Compute, Section for Cognitive Systems and also closely collaborate with Prof. Rasmus Bro and Assoc. Prof. Michael Kastoryano at the University of Copenhagen Department of Food Science and Department of Computer Science respectively. The position is financed by the Novo Nordisk Foundation Data Science Collaborative Grant “Tensor Networks for Data Science”. 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 two years. You can read more about career paths at DTU here. Further information may be obtained from Professor Morten Mørup (DTU Compute) at mmor@dtu.dk. The PostDoc project will be hosted at the Section for Cognitive Systems (www.compute.dtu.dk/sections/cogsys) at DTU Compute (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 the 31 October 2024 (23:59 Danish time). Apply online here: Postdoc in Machine Learning using Tensor Networks 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 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. *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. *The section for Cognitive Systems at DTU Compute* The position is in the Section for Cognitive Systems at DTU Compute, the Technical University of Denmark, which is a top Danish machine learning group. Both salary and working conditions are excellent. The group is a down-to-earth and fun place to be. Most group members live in Copenhagen, which is often named as the best city in the world to live, and for good reasons. It's world renowned for food, beer, art, music, architecture, the Scandinavian "hygge", and much more. Parental leave is generous and child-care is excellent and cheap. You can read more at Section for Cognitive Systems - DTU Compute. *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. If you apply for this position please say you saw it on Computeroxy

Postdoc in Machine Learning using Tensor Networks - DTU Compute

Are you interested in developing novel machine learning methodologies that are scalable, reliable and explainable and that can address imminent challenges both within quantum physics and the life-sciences?

As our new colleague your job will be to develop novel computational frameworks for machine learning by use of tensor network representations. In particular, you will push the boundaries of

Scalability, drawing upon recent large-scale TN capabilities in physics.
Reliability, exploring uncertainty quantification and robust inference in machine learning
Explainability, leveraging identifiability and unique recovery explored using tensor decompositions within the life-science domain.

A Tensor Network (TN) is a data structure for representing high-dimensional arrays (tensors) in a low-rank format as a sequence of smaller cores which can be stored and manipulated efficiently. TNs provide promising tools for large-scale machine learning, as they allow for exponential savings in memory and processing time, while often allowing for explainable structure extraction. However, key obstacles remain, preventing their widespread use. These include limitations in terms of reliable large-scale inference, uncertainty quantification, as well as efficient TN structure identification, assessment, and interpretation. You will address these obstacles and demonstrate how the developed tools can address important challenges within quantum physics as well as large-scale life-science data modeling.

Responsibilities and qualifications
You are fascinated by machine learning and how computers can learn from data and you have a strong interest in the mathematical foundation of machine learning models. In this position, you will be responsible for developing novel tensor network based machine learning methodologies enabling new approaches for solving supervised, unsupervised and reinforcement learning problems. You will thereby leverage tensor networks as a computationally efficient and expressive framework that provides a complimentary generic framework to deep learning.

Specifically, we are looking for a profile with the motivation and drive needed for making a difference that matters. You must bring an open mindset and like to create results via collaboration with multiple people with different professional and cultural backgrounds. You are a talented, self-motivated, and team-oriented person who enjoys working on the theoretical foundation of machine learning. In particular, your CV comprises:

A strong relevant background within machine learning and mathematics.
Extensive experience programming in Python (or Matlab).
An active interest or experience in strong collaborations and interdisciplinary work at the intersection between machine learning, physics and life-science data modeling.
You must be fluent in English, both speaking and writing and possess excellent communication skills.

You will be supervised by Professor Morten Mørup at DTU Compute, Section for Cognitive Systems and also closely collaborate with Prof. Rasmus Bro and Assoc. Prof. Michael Kastoryano at the University of Copenhagen Department of Food Science and Department of Computer Science respectively. The position is financed by the Novo Nordisk Foundation Data Science Collaborative Grant “Tensor Networks for Data Science”.

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 two years.

You can read more about career paths at DTU here.

Further information may be obtained from Professor Morten Mørup (DTU Compute) at mmor@dtu.dk.

The PostDoc project will be hosted at the Section for Cognitive Systems (www.compute.dtu.dk/sections/cogsys) at DTU Compute (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 the 31 October 2024 (23:59 Danish time). Apply online here: Postdoc in Machine Learning using Tensor Networks

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

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.

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.

The section for Cognitive Systems at DTU Compute
The position is in the Section for Cognitive Systems at DTU Compute, the Technical University of Denmark, which is a top Danish machine learning group. Both salary and working conditions are excellent. The group is a down-to-earth and fun place to be. Most group members live in Copenhagen, which is often named as the best city in the world to live, and for good reasons. It's world renowned for food, beer, art, music, architecture, the Scandinavian "hygge", and much more. Parental leave is generous and child-care is excellent and cheap. You can read more at Section for Cognitive Systems - DTU Compute.

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.