Now Hiring: 4 PhD Fellows in Deep Learning

The Department of Physics and Technology at UiT The Arctic University of Norway is pleased to announce 4 exciting PhD Fellowships within machine learning at SFI Visual Intelligence. Application deadline: June 17th.

By Petter Bjørklund, Communications Advisor at SFI Visual Intelligence

The positions are for a period of three years. The objective of the position is to complete research training to the level of a doctoral degree. Admission to the PhD programme is a prerequisite for employment, and the programme period starts on commencement of the position.

For the full information about the positions, click the image to the right.

The position’s field of research

As a VI researcher, you will contribute to solving the pressing societal challenges of our time for a sustainable future. You will contribute important new solutions within healthcare and precision medicine, be at the forefront in marine ecosystem monitoring by AI, enable novel methods for more efficient use of energy resources or infrastructure, and help develop better ways to observe the Earth from space to benefit the planet and to aid decision-making.

This will be done by collaborating with VI consortium partners from industry and the public sector to create new innovations to benefit Norwegian value creation.  

3 of the PhD positions are funded through the center's budget, and one position is funded by a UiT's interdisciplinary project called the Consortium for Patient-Centered AI (CPCAI).

For all 4 positions, potential directions are to research new ways to

• Represent the general properties of relevant and real-world data by self-supervised learning towards AI foundation models.
  ‍
• Represent general properties of data coming from different sources, i.e. multimodal AI models (combining images, text, etc).
  ‍
• Understand the important mechanisms of the AI models in terms of their prototypical behaviour, individual neurons or layers within networks, or the quality of the data (data-centric intelligence).
  ‍
• Develop interpretable generative AI solutions.
  ‍
• Develop new methodology to improve the robustness and reliability of deep learning models.

Each of the 4 positions has a different innovation area:

• Position 1: This position will have a medical and health innovation focus and will collaborate with one or more of the consortium’s health partners: the University Hospital of North Norway, The Cancer Registry of Norway, GE Healthcare. The candidate will develop new solutions in one or more areas such as (multimodal) MR and/or CT-based tumor segmentation and quantification, mammography-based breast cancer, or cardiac ultrasound for early detection of heart diseases.
  ‍
• Position 2: This position will have an energy innovation focus and will collaborate with one or more of the consortium’s energy partners: Equinor and Aker BP. The candidate will potentially work on energy foundation models based on seismic data for more efficient use of energy resources, may develop methods for characterizing the subsurface within palynology (e.g. digitized microfossil analysis), or be engaged in energy infrastructure monitoring.  
  ‍
• Position 3: The innovation area for this position will be determined upon examination of the applicants with respect to methodological research potential or based on the consortium’s needs. Relevant innovation areas are within marine ecosystem monitoring, within medicine and health, within energy, earth observation, or a combination of use cases from all these application areas.
  ‍
• Position 4 (CPCAI): The innovation area of this position is within deep learning for decision and diagnosis support by analysis of data from electronic health records (EHRs). This position is conducted in collaboration with the University Hospital of North Norway and the CPCAI project. EHRs are by nature multimodal and the safe exploitation of EHRs are key for the future of the healthcare system. Potential directions include best possible multimodal representation learning in EHRs, predictions of adverse outcomes after surgery, and causal discovery (treatment-effect-counterfactuals).

Important

• For all positions, transfer of methodology and collaboration across application areas are aimed for. The 4 PhD candidates will collaborate and create synergies on the core deep learning methodological research.
• You must indicate any preferences you may have with respect to the 4 positions.

A detailed work plan and project description for the PhD candidate will be devised in a collaboration between the fellow, the research team and the supervisors, as well as the consortium partners.

Qualifications

We are particularly seeking candidates with solid background in machine learning methodology, in terms of the mathematical and statistical foundation of such methods. We are seeking candidates with course work and experience in deep learning, neural networks and machine learning, e.g. self-supervised learning, convolutional neural networks, transformer-based networks, eigenvalue/eigenvector-based methods, graph-based approaches, Bayesian learning, information theory, geometric methods or neural operator learning.

Required qualifications:

• You must hold a Master’s degree in machine learning or related relevant fields within e.g. mathematics, statistics, computer science, physics, electrical engineering.
  ‍
• A strong formal course background in deep learning and machine learning in general or relevant topics such as pattern recognition or computational statistics is required. Important topics are described above.
  ‍
• Documented programming skills, for example using Python, etc.
  ‍
• Good communication skills in English are necessary and documented fluency in English is required. Nordic applicants can document their English capabilities by attaching their high school diploma.

Preffered qualifications:

• Research experience via Master thesis or internships or similar involving development of deep learning and machine learning methodology and applications.
• Experience with software tools such as e.g. PyTorch, Keras, Tensorflow, and Jax.
  ‍
• Experience with collaborative coding, e.g. via Git/GitHub.
  ‍
• For position 4 (CPCAI) good oral and written command of a Scandinavian language is considered an advantage, given that free text in EHRs will be in Norwegian if working with EHR data from the University Hospital of North Norway.

Desired qualifications:

• Experience in interdisciplinary and collaborative research, given that all positions aim for innovation and collaboration.
  ‍
• Any relevant scientific publications.
  ‍
• Abilities to be creative and be able to take on and develop own initiatives.

We will also emphasize motivation and personal suitability for the position. We are looking for interested, active and highly motivated candidates, who like to explore new technologies, are both independent thinking and enjoy working in a collaboration with others. We hope this is you!

In the assessment, the emphasis is on the applicant's potential to complete a research education based on the master's thesis or equivalent, and any other scientific work. In addition, other experience of significance for the completion of the doctoral programme may be given consideration.

As many people as possible should have the opportunity to undertake organized research training. If you alreay hold a PhD or have equivalent competence, we will not appoint you to this position.

Want to know more about the positions?

For enquiries about for the position, please contact:

• Director of Visual Intelligence, Professor Robert Jenssen: robert.jenssen@uit.no
• ‍Head of Department of Physics and Technology, Professor Olav Gaute Hellesø: olav.gaute.helleso@uit.no.

Principal Investigators at Visual Intelligence and UiT Machine Learning Group members

• Professor Michael Kampffmeyer: michael.k.kampffmeyer@uit.no
• Professor Fred Godtliebsen: fred.godtliebsen@uit.no
• Associate Professor Elisabeth Wetzer elisabeth.wetzer@uit.no
• Associate Professor Kristoffer Wickstrøm: kristoffer.k.wickstrom@uit.no
• Associate Professor Karl Øyvind Mikalsen: karl.o.mikalsen@uit.no

For the full information about the positions, click the image below.