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PhD Candidate - ESR 15: Federated deep active learning for AI-based interpretation of lumbar MRI in discogenic LBP patients w/ & w/o data virtual enhancement

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    Universitat Pompeu Fabra - ETIC
    Computer science
    EngineeringComputer engineering
    First Stage Researcher (R1)
    01/10/2021 23:00 - Europe/Athens
    Finland › OULU
    H2020 / Marie Skłodowska-Curie Actions


The European community requires early stage researchers (ESRs) who can work across the boundaries of traditional disciplines, integrating experimental and in silico approaches to understand and manage highly prevalent multifactorial disorders, such as musculoskeletal disorders. The Disc4All training network utilises intervertebral disc degeneration (LDD) leading to low back pain (LBP) as a relevant application for the integration of data and computational simulations in translational medicine, to enable rational interpretations of the complexity of the interactions that eventually lead to symptoms. 

LBP is the largest cause of morbidity worldwide, yet there remains controversy as to the specific cause leading to poor treatment options and prognosis. LDD is reported to account for 50% of LBP in young adults, but the interplay of factors from genetics, environmental, cellular responses and social and psychological factors is poorly understood. Unfortunately, the integration of such data into a holistic and rational map of degenerative processes and risk factors has not been achieved, requiring creation of professional cross competencies, which current training programmes in biomedicine, biomedical engineering and translational medicine fail to address, individually. 

Disc4All aims to tackle this issue through collaborative expertise of clinicians; computational physicists and biologists; geneticists; computer scientists; cell and molecular biologists; microbiologists; bioinformaticians; and industrial partners. It provides interdisciplinary training in data curation and integration; experimental and theoretical/computational modelling; computer algorithm development; tool generation; and model and simulation platforms to transparently integrate primary data for enhanced clinical interpretations through models and simulations. Complementary training is offered in dissemination; project management; research integrity; ethics; regulation; policy; business strategy; and public and patient engagement. The Disc4All ESRs will provide a new generation of internationally mobile professionals with unique skill sets for the development of thriving careers in translational research applied to multifactorial disorders.

Hiring Institution

  • Hiring Disc4All Member: University of Oulu, Finland
  • Web: www.oulu.fi
  • Address: Aapistie 5A, POB 5000, FI-90014 UNIVERSITY OF OULU, FINLAND
  • Type of contract: Fixed-term (36 months)
  • Job status: Full-time
  • Hours per week: 36,75 h
  • Offer starting date: September 2021
  • EU Research Framework: H2020 MSCA-ITN-ETN
  • Marie Curie Grant Agreement Number: 955735

Topic: Federated deep active learning for AI-based interpretation of lumbar MRI in discogenic LBP patients w/ & w/o data virtual enhancement.

Description: In this project, the successful candidate will develop the next generation of Deep Learning methods for automatic and interpretable analysis of lumbar spine MRI data. The PhD thesis will contribute to the Disc4All Project in terms of advanced low back pain patient stratification, and it aims to develop tools, allowing the learning from data with and without annotations, and from different sources (i.e. in a federated learning setup). As we will also develop new ways to interpret low back pain phenotypes of medical images, both real world and (interpretable) simulation-based data will be progressively integrated, and models will be trained through active learning. Hence, the doctoral student is expected to contribute to progress in generic machine learning, computer vision and MRI-based interpretable diagnoses for low back pain.

  • Enrolment in Doctoral degree(s): Medical Physics and Technology
  • Supervisors: Dr. Aleksei Tiulpin, PhD PhD; Prof. Simo Saarakkala, PhD; Prof. Jaro Karppinen, MD, PhD
  • Host & PhD delivered by: University of Oulu
  • Start date: September 2021, Duration: 36 months
  • Hosting lab: Research Unit of Medical Imaging, Physics and Technology
  • Location: Faculty of Medicine, University of Oulu
  • Web: www.oulu.fi/mipt/



More Information



The MSCA programme offers a competitive salary and attractive working conditions, in accordance with the MSCA regulations for early stage researchers.

You will be enrolled in the PhD programme of Medicine (Medical Physics and Technology) of the University of Oulu, and have the opportunity to learn from a consortium of 19 institutions (11 Beneficiaries, 8 Partner organizations). In addition to the individual scientific projects, all ESRs will benefit from further continuing education, which includes secondment to Universitat Pompeu Fabra (Barcelona, Spain) and Plexalis Ltd (Oxford, United Kingdom), a variety of training courses for specific and transferable skills, and active participation and international conferences.

Successful candidates will be offered a 36 months full-time employment contract, with an monthly salary of 3950 € (average gross salary, before statutory deductions); plus an additional mobility allowance (600€ per month, unconditional), and an additional family allowance (500€ per month, if applicable).


Eligibility criteria


a) To apply for these MSCA Training positions, applicants must fulfil the following criteria:

  • Mobility: to be eligible for a position, you should not have resided in the country of the host institution for more than 12 months over the three years before the starting date of the position, excluding holidays and (refugee status) asylum application.
  • Early Stage Researcher (ESR): At the time of recruitment by the host organisation, an ESR shall be in the first four years (full-time equivalent research experience) of his/her research career and not have been awarded a doctoral degree.

Candidates must prove that they fulfil the aforementioned criteria through relevant documentation (certificates, official statements, residency card, …).


b) Applicants will also be required to successfully complete the relevant local application process at the host institution: N/A


c) Specific requirements for the proposed project:

  • Educational Level: Master degree or equivalent
  • Required languages: Sufficient language skills in English (see www.oulu.fi/uniogs/requirements_for_admission)
  • Skills/Qualifications: The candidate must demonstrate experience in machine learning, knowledge of PyTorch or equivalent frameworks, advanced knowledge of Python and computer programming in general, and good knowledge of probability, calculus, and algebra.
  • Eligibility to enrol in the PhD programme at the University of Oulu. See the following web page for more details: www.oulu.fi/uniogs/requirements_for_admission.

Selection process


The selection committee uses a number of indicators to evaluate the applicant’s preparedness, motivation and potential.

1st phase, remote pre-selection:

The Scientific, Technological & Academic excellence will be considered at first, based on:

  • Quality of the CV, in general
  • Any demonstrated research experience, particularly if supported by evidence such as scientific publications in recognized machine learning or medical image analysis venues.
  • Undergraduate performance: overall, with a special focus on mathematics, physics, and computer programming
  • Any demonstrated previous recognitions (e.g. grants, awards, kaggle and ACM ICPC medals, etc)
  • Reference letters provided by professors and senior scientists: Three reference letters are expected. At least two letters must be issued by scholars. The third letter can be provided either by a scholar or by a relevant professional of the industrial sector. Referees are asked to address analytical capabilities, technical proficiency, ability to work independently and motivation/commitment.
  • Statement of purpose: past research experience, motivation for applying to this particular PhD project, academic fit, contribution of the project to the candidate’s future careers plans, ...
  • Additional relevant skills and achievements, e.g. industry experience and industrial projects completed, open-source contributions

2nd phase, interview(s):

Should the candidate be preselected at phase 1 (5 candidates will be selected), a second phase will consist of two interviews:

  1. The first interview will evaluate the technical competence of the candidates. Two candidates will be selected based on this step.
  2. The second interview will evaluate the motivation, proactive behaviour, capacity to work independently and collaboratively, organizational skills, communication skills and the capacity to engage in a scientific discussion and manage problems.

The final decision will be the result of a consensus of an evaluation committee that will take into account the results of both recruitment phases 1 and 2. The candidate will be informed of the section results by email.


Additional comments


All the documents that prove the eligibility of the candidate should be provided. As for the selection process candidates are expected to provide at least the following documents:

  • A brief introduction letter (max. one A4 page) that summarizes the documents and the nature of the information provided for the selection
  • A full CV (max. 2 pages)
  • The three requested reference letters
  • The letter of purpose (max. two A4 pages)

All documents must be sent by email to Dr. Aleksei Tiulpin (aleksei.tiulpin@oulu.fi) by email with a subject topic “[Disk4All] FistName_LastName]” and to the Management of the Disc4All project (disc4all@upf.edu) before June 20th, 2021.


Work location(s)
1 position(s) available at
University of Oulu

EURAXESS offer ID: 640980


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