Publisher
source

Karim Abdoun

5 months ago

PhD Position in AI-Enhanced Epidemiological Modeling and Control Pázmány Péter Catholic University in Hungary

Degree Level

PhD

Field of study

Computer Science

Funding

Fully funded PhD position for up to 4 years with a competitive scholarship of approximately EUR 1,500–2,100 per month. Includes access to HPC infrastructure, travel funding for conferences and consortium meetings, and potential research stays with partner institutions.

Deadline

Expired

Country flag

Country

Hungary

University

Pázmány Péter Catholic University

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Keywords

Computer Science
Machine Learning
Biology
Mathematics
Artificial Intelligence
Disease Modeling
Reinforcement Learning
Salud Pública
Optimisation
Statistics

About this position

PhD opportunity at Pázmány Péter Catholic University (PPKE), Faculty of Information Technology and Bionics, Budapest, Hungary, within the Horizon Europe project AURORAI (Advancing Pandemic Preparedness and Response through High-Quality Data and AI).

The project sits at the intersection of AI, epidemiological modeling, control theory, machine learning, and agent-based modeling. The successful candidate will work on behaviour-aware, AI-enhanced outbreak prediction using the PanSIM microsimulation framework, combining Python-based ML methods with the C++ simulation core and scaling from city-level to national-level dynamics using HPC.

Supervision and collaboration involve Prof. Gábor Szederkényi, Prof. Attila Csikász-Nagy, Prof. István Reguly, and Dr. Péter Polcz, in a 14-partner European consortium spanning academic, clinical, and industrial partners. The role includes designing reinforcement learning and model predictive control strategies, integrating demographic and mobility data, and contributing to scientific publications and conference presentations.

Eligibility highlights: applicants should have a Master’s degree or equivalent in Control Engineering, Machine Learning/Data Science, Applied Mathematics, Computer Science, Bioengineering, or a related field; strong mathematical foundations; proficiency in Python and ML ecosystems; and English proficiency. A working knowledge of C++ is required or expected to be learned quickly. Desirable experience includes mathematical epidemiology, population dynamics, ABMs, HPC, and parallelization.

Funding: fully funded PhD for 3–4 years, with a competitive stipend of approximately €1,500–2,100/month, plus access to HPC resources, travel support, and possible research stays.

Application deadline: 31 May 2026. Start date: September 2026. Applicants should submit a single PDF by email to [email protected] including CV, cover letter, and 1–2 academic references.

Funding details

Fully funded PhD position for up to 4 years with a competitive scholarship of approximately EUR 1,500–2,100 per month. Includes access to HPC infrastructure, travel funding for conferences and consortium meetings, and potential research stays with partner institutions.

What's required

A Master's degree or equivalent by the start date in Control Engineering, Machine Learning/Data Science, Applied Mathematics, Computer Science, Bioengineering, or a related interdisciplinary field; solid mathematical foundations in machine learning, control theory, optimization, or dynamical systems; strong proficiency in Python and ML tools such as PyTorch, TensorFlow, or Scikit-learn; working knowledge of or willingness to learn C++; experience with complex datasets, statistical analysis, and dynamic systems modeling; proficiency in written and spoken English. Desirable: background or interest in mathematical epidemiology, population dynamics, or complex systems; prior experience with agent-based models; familiarity with HPC environments or parallelization concepts such as OpenMP or CUDA; experience with AI-based coding tools.

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Professors

Prof.

University Name
Country flag

Hungary

Research Interests

Artificial Intelligence

Statistics

Mathematics

Biology

Optimisation

Prof.

University Name
Country flag

Hungary

Research Interests

Artificial Intelligence

Statistics

Biology

Optimisation

Computer Science

Prof.

University Name
Country flag

Hungary

Research Interests

Epidemiology

Fluid Mechanics

High Performance Computing

Sars-cov-2

Covid-19