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"PhD Position for Advanced Nearest Neighbour Models for Active Matter"

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PhD Position for Advanced Nearest Neighbour Models for Active Matter

Job Summary

This PhD position involves developing new mathematical theory motivated by active matter applications, using graph theory in nearest neighbor models. The project aims to extend statistical mechanics tools to applications in soft and active matter, incorporating multi-body interactions and dynamic graphs for cell sheet models and experimental systems.

Responsibilities

  • Participate in scientific activities, meetings, and workshops of the EAAS consortium.
  • Form interdisciplinary collaborations across fields and locations in the Netherlands.
  • Acquire skills in science communication, interacting with public audiences, and high school science teaching.
  • Use graph theoretical tools to develop new frameworks and analytic tools for statistical mechanics models.
  • Test applications on artificial and real data.
  • Write scientific texts and publish in peer-reviewed journals.
  • Present work in seminars, workshops, and conferences.
  • Attend seminars and discussion days at participating institutes.
  • Participate in teaching at the KdVI and supervise Bachelor and Master student projects.
  • Participate in the Faculty of Science PhD training program.
  • Complete and defend a PhD thesis within four years.

Qualifications and Requirements

  • A Master's degree in mathematics or a joint Master's including mathematics (e.g., math/physics or math/biology).
  • Good command of spoken and written English, especially in writing formal mathematical proofs.
  • Ability to work productively both independently and in a team.
  • Experience or interest in teaching with an encouraging style.

What the Employer Offers

  • A temporary contract for 38 hours per week for 4 years (initially 18 months, extendable after evaluation).
  • Preferred starting date as soon as possible, but no later than 1 January 2026.
  • An educational plan including courses, meetings, and teaching assistance for undergraduates and master students.
  • Work in an interdisciplinary team at the Korteweg-de Vries Instituut voor Wiskunde (KdV Institute) at the University of Amsterdam.
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