PhD Student (f/m/d) in Quantum Algorithms for Droplet and Bubble Oscillation Dynamics ModellingFull PhD

Working Language
English
Location
Dresden
Application Deadline
19 Mar 2026
Starting Date
01 Apr 2026

Overview

Open Positions

1

Time Span

01 Apr 2026 for 3 years

Application Deadline

19 Mar 2026

Financing

yes

Type of Position

Full PhD

Working Language

English

Required Degree

  • Diplom
  • Master

Areas of study

Physics, Applied Mathematics

Description

Description

With cutting-edge research in the fields of ENERGY, HEALTH and MATTER, around 1,500 employees from more than 70 nations at Helmholtz-Zentrum Dresden-Rossendorf (HZDR) are committed to mastering the great challenges facing society today.

The Center for Advanced Systems Understanding (CASUS) is a German-Polish research center for data-intensive digital systems research. The Institute of Fluid Dynamics conducts both fundamental and applied research in the fields of thermo-fluid dynamics and magnetohydrodynamics to enhance the sustainability, the energy efficiency, and the safety of industrial processes.

In a joint effort of both institutes, the Department AI4Quantum – Machine Learning for Quantum Simulation and Computing and Thermal Energy and Process Engineering are looking for a PhD Student (f/m/d) in Quantum Algorithms for Droplet and Bubble Oscillation Dynamics Modelling. 

Your tasks

  • Development and implementation of numerical and algorithmic methods for the simulation of fluid-dynamical or environmental systems
  • Research on quantum and hybrid quantum–classical algorithms for solving partial differential equations
  • Implementation, testing, and benchmarking of computational methods on high-performance computing and quantum computing platforms
  • Analysis and validation of results using classical reference models and simulation data
  • Documentation of research results and contribution to scientific publications and project reports

Your profile

  • Completed university studies (Master/Diploma) in the field of Physics, Applied Mathematics, Computer Science, Computational Modeling and Simulation or related field
  • Knowledge of Numerical solution of partial differential equations
  • Knowledge of Scientific computing / high-performance computing
  • Knowledge of Algorithmic modeling and simulation
  • Fundamentals of machine learning or quantum computing
  • Programming skills in Python and/or C/C++
  • Experience with scientific software tools and numerical libraries
  • Familiarity with Linux-based computing environments
  • Ability to work independently and in a structured manner
  • Strong analytical thinking and problem-solving skills
  • Willingness to collaborate in interdisciplinary research teams
  • Motivation to engage in scientific research and method development
  • Good written and oral communication skills
  • English language proficiency (written and spoken)

Our offer

  • A vibrant research community in an open, diverse and international work environment
  • Scientific excellence and extensive professional networking opportunities
  • A structured PhD program with a comprehensive range of continuing education and networking opportunities - more information about the PhD program at the HZDR can be found here
  • Salary and social benefits in accordance with the collective agreement for the public sector (TVöD-Bund) including 30 days of paid holiday leave, company pension scheme (VBL)
  • We support a good work-life balance with the possibility of part-time employment, mobile working and flexible working hours
  • Numerous company health management offerings
  • Employee discounts with well-known providers via the platform Corporate Benefits
  • An employer subsidy for the "Deutschland-Ticket Jobticket"

We look forward to receiving your application documents (including cover letter, CV, diplomas/transcripts, etc.), which you can submit via our online-application-system: https://www.hzdr.de/db/Cms?pNid=490&pLang=en&pOid=76871

Required Documents

Required Documents

  • CV
  • Motivation letter
  • Certificates
  • Transcripts
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