Phd: Learning Interactions In Collective Cell Migration

ScholarLink Inc.

Netherlands

Remote

EUR 268,000 - 335,000

Full time

14 days+

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Job summary

ScholarLink Inc. in the Netherlands invites applications for a four-year PhD student position within the Broedersz group (Theoretical physics of life) focusing on non-equilibrium statistical physics of collective cell migration.

The project emphasizes data-driven theoretical frameworks and inference methods, with opportunities to publish in high-impact journals and present at international conferences. Applicants should have a physics background (theoretical/statistical) and strong programming

Qualifications

  • Background in Physics, preferably theoretical or statistical physics.
  • Experience with inference/machine learning tools and programming is a plus.

Responsibilities

  • Conduct research on non-equilibrium statistical physics of collective cell migration.
  • Develop and apply data-driven models and inference tools.
  • Publish research findings in high-impact journals.
  • Present work at international conferences.

Skills

Physics background
Inference tools
Programming
Data-driven modeling

Education

Physics (theoretical/statistical)

Tools

Python

Job description

About the Position

The Broedersz group (Theoretical physics of life) has an opening for a four-year PhD student position on the non-equilibrium statistical physics of collective cell migration.

Research Interests
  • Conduct research on non-equilibrium statistical physics of collective cell migration.
  • Develop and apply data-driven models and inference tools.
  • Publish research findings in high-impact journals.
  • Present work at international conferences.
Required Skills & Qualifications
  • Background in Physics, preferably theoretical or statistical physics.
  • Experience with inference/machine learning tools and programming is a plus.
Other Requirements & Additional Info

A Vici funded 4-year PhD position focused on uncovering fundamental interaction rules in collective cell migration using data-driven theoretical frameworks.

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