Sano PhD Student: Data abstractions as the foundation of machine self-learning from different data...

Updated: 2 months ago
Job Type: FullTime
Deadline: 31 Dec 2021

Project title: Data abstractions as the foundation of machine self-learning from different data sources

Publication date: 22.10.2021

Closing date: 31.12.2021

Level of education: Master's degree

Hours: 40 hours per week

Salary indication: up to 8000 PLN gross monthly

Supervisors:

Sano: Dr. Jose Sousa (Research Group Leader – Personal Health Data Science)

Project start: January 2022 (depending on candidate’s availability)

Data is at the forefront of any process of decision making, as if there was any doubt, the actual pandemic made it clear. Data collection happens every day with individual contribution from browsing keywords to the sharing of images and feelings. This created a promised that if you collect enough data to generate big data, machines will be able to model the world and provide unique insights. However, the reality is not perfect and collecting “all the data” to describe a problem is impossible within a chaotic reality. One of the most promising approaches to the development of personalized medicine became siloed in machine learning applied to single diseases because of the difficulty on accessing and integrating data sources. This is closed related and defines the evolution of the actual approaches on machine learning.

Within this PhD, algorithms will be built to develop data abstraction in the local systems and upload them to the machine self-learning environment. This would allow the integration of different data sources in a way not done until now allowing the evolution of machine learning.

The project will be carried out in cooperation in computer scientists and machine learning from Sano and its partners and data sources providers such as polish hospitals and develop collaborations to explore data repositories in the UK and in Europe.

What are you going to do?

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.

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