Job
- Level
- Experienced
- Location
- Kiel
- Working Model
- Onsite
- Job Field
- Data
- Employment Type
- Full Time
- Contract Type
- Temporary employment
Job Summary
In this role, you develop data-driven surrogate models to accelerate physics-based simulations for aneurysm treatment, implement ML pipelines, and evaluate their accuracy and efficiency.
Job Technologies
Your role in the team
- Develop data-driven surrogate models to replace computationally expensive physics-based simulations for aneurysm treatment, namely, computational fluid dynamics (CFD) and finite element modeling (FEM), enabling rapid, on-demand simulation of patient-specific treatment scenarios.
- Design and implement machine learning (ML) pipelines using full-fidelity CFD and FEM data, ensuring models generalize across patient geometries and device configurations.
- Integrate trained surrogate models into the broader in silico trial framework and evaluate their accuracy, robustness, and computational efficiency against full-fidelity simulations.
- Explore AI-based methods for treatment outcome prediction and device optimization, contributing to the framework's clinical decision-support capabilities.
- Collaborate closely with clinicians, scientists, and engineers to develop and validate cutting-edge simulation techniques.
- Leiten und Co-Autor wissenschaftlicher Veröffentlichungen, sowie Vertretung des Projekts auf internationalen Konferenzen.
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Our expectations of you
Qualifications
- PhD in Computer Science, Applied Mathematics, Physics, Engineering, or a related field with a strong computational focus.
- Proficiency in Python and relevant ML frameworks (e.g., PyTorch, TensorFlow, JAX) is essential.
- Familiarity with CFD, FEM, or other numerical simulation methods is a significant advantage.
Experience
- Starke praktische Erfahrung im maschinellen Lernen für wissenschaftliche Anwendungen – einschließlich Surrogatmodellierung, Reduced-Order-Modelle (ROM) oder physics-informed machine learning (PIML/PINNs) – bildet die technische Kernkompetenz der Rolle.
- Experience with high-performance computing (HPC) environments is beneficial.
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What we offer
- The salary will be based on the German E13 TV-L scale (100%), if the terms and conditions under collective bargaining law are fulfilled.
- A full-time employment, currently 38.5 hours/week; part-time employment may be possible within the framework of certain working time models.
- Flexible working hours to accommodate individual needs.
- Interdisziplinäre Forschung an der Schnittstelle von Medizin, Physik, Ingenieurwesen und Informatik.
- A vibrant, international biomedical research group embedded within a university hospital setting.
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Benefits
Work-Life-Integration
Higher Take-Home Pay
Topics You Will Work On
Job Locations
About Your Employer
Universitätsklinikum Schleswig-Holstein
The Schleswig-Holstein University Hospital - UKSH - is one of the largest European centers of university medicine - together with the Christian-Albrechts University in Kiel and the University of Lübeck - international top research with interdisciplinary patient care and the education of doctors of future generations.
Description
- Company Size
- 250+ Employees
- Company Type
- Established Company
- Working Model
- Hybrid, Onsite
- Industry
- Healthcare, Social Sector
Employer reviews
by devworkplaces.com
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(1 Review)3.1
Engineering
3.0Workingconditions
3.0Career Growth
2.7Culture
3.7
Postdoctoral Position Machine Learning for Simulation Acceleration
- Location
- Kiel
- Working Model
- Onsite
- Diversity
- Open for all genders
