Sano PhD students

Application of Federated Learning to Medical Data at Large Scale

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Sano Centre for Computational Medicine is a new International Scientific Foundation located in Kraków, Poland.

Sano aspires to be a major translational scientific institute, operating at the meeting point of academic science, established MedTech industry, and emerging start-up environment, combining the best of these three perspectives.

Sano acts as a core technology and expertise provider for industry, and creator of innovation, developing state-of-the-art solutions for healthcare. Thanks to the substantial funding and excellent European partnership network, Sano will bring a critical mass to this transformational field of research, in order to translate scientific advancements onto clinical practice. Sano’s ambition is to become the Reference Centre for Computational Medicine in Central Europe and build a reputation as a leading institute on a global level.

As a cross-disciplinary institution, Sano uses machine learning/artificial intelligence (ML/AI), large scale computer simulations (HPC), data science, and other computational technologies towards overcoming global challenges in healthcare systems. The research agenda will be executed in close collaboration with Partners in Poland, EU and USA.

We value:

  • Passion – Passion in what we do, engagement in Sano operations, taking responsibility, providing initiative, being happy at work.
  • Innovation – Boldness in articulating and pursuing novel ideas, courage to think outside the box.
  • Integrity – Directness, openness, tolerance and respect. Scientific integrity (we do not cut corners).
  • Diversity – Diversity in backgrounds, cultures and opinions of Sano employees. Promotion of women in STEM.

Federated learning is a technique which allows training machine learning models in a distributed way without transferring the data from its source. It has thus potential applications in analysis of medical data, where privacy and security issues are of great importance. Although there are examples of using federated approaches to analysis of medical data, there is still need for research in this area and for experiments in real distributed environments.
The goal of the thesis will be to apply federated learning techniques to selected problems in area of medical data analysis in collaboration with other Sano research teams. Examples of the data are coming from the various areas of research at Sano, including but limited to:

  • Medical image classification and segmentation,
  • Individual patient data coming from mobile applications and wearable devices,
  • Genomics data from next generation sequencing,
  • Training of medical robots.

The analysis will be performed using distributed computing frameworks such as Flower, FedML or Nvflare, using distributed computing infrastructures such as PL-Grid, EuroHPC or public cloud services. The main challenge will be to evaluate how the large scale of datasets influences the machine learning process, and how to optimize it for such scenarios.
In addition to evaluation of the learning process, the goal will be also to evaluate the performance of distributed computing environment. Various metrics related to the distribution of data, its granularity and partitioning will be investigated, to understand their impacts on the both the efficiency of the learning process and the performance of the infrastructure. Further study will include also possible attacks and security of the developed solution. Other types of data and machine learning tasks can be considered for comparison as well.

You are expected to: 

• do original research in this field under the direction of the supervisor; 
• participate in the many seminars by internal and external speakers as well as journal clubs and group activities; 
• collaborate with other PhD candidates, postdoctoral researchers and other Sano employees. 

Required background and skills of the candidate:

  • Master’s degree (completed or near completion) or 1st Class undergraduate Honours degree in a relevant discipline such as computer science, computer engineering, robotics, physics or similar;
  • experience in Machine Learning frameworks and tools;
  • knowledge of distributed computing, cloud technologies, big data analytics is desired;
  • excellent programming skills: Python, C++, Bash;
  • prior academic or industry research experience;
  • interest in pursuing applied research;
  • excellent written and oral English communication skills.

We offer a fixed term contract for 40 hours per week for the duration of 4 years. This will be supported by an educational plan that includes attendance of courses and (international) meetings. The contract will include opportunities to participate in teaching and supervision of undergraduate and master students.

The salary, depending on relevant experience before the beginning of the employment contract, will be up to 8000 PLN gross per month, based on a full-time contract (40 hours a week) for the duration of 4 years with private medical care and a sports card.

This PhD will be a collaboration between Sano and a degree awarding body. The student will be based in Poland, Krakow operating in conjunction with the Cyfronet supercomputing centre hosted by AGH.

Sano offers excellent opportunities for study and development, an access to many international conferences on computational medicine and a possibility to grow in a scientific society.

If you feel that this job opportunity is the career that you are looking for, please apply immediately and give Sano a chance to meet you. Please, be informed that Sano will contact only with Candidates fulfilling the formal criteria.

Sano is an equal-opportunity employer. We prioritize diversity and are committed to creating an inclusive environment for everyone. We value a spirit of enquiry and perseverance, provide the space to keep asking questions, and promote a culture of curiosity and creativity.

Do you recognize yourself in the job profile? Then we look forward to receiving your application.

Applications in .pdf should include:

  • a cover letter;
  • a curriculum vitae;

Apply through Sano recruitment system. If you would like to apply for future openings feel free to send your application to our database.​