Name of the host institution: ELTE Eötvös Loránd University
Faculty: Faculty of Science
Name of the supervisor: Máté Csanád
Organisational Unit / Research Group: Femtoscopy Research Group
Research Team

• Máté Csanád (professor; femtoscopy, QCD phase diagram, hydrodynamics, flow)
• Márton Nagy (femtoscopy, hydrodynamics, complex analysis)
• Dániel Kincses (postdoctoral researcher; femtoscopy, QCD phase diagram)
• Barnabás Pórfy (PhD student, femtoscopy, QCD phase diagram, data aquisition)
• + several other students working on various fields
  https://phenix.elte.hu/
  https://star.elte.hu/
  https://normadiagnostika.com/norma-instruments-and-elte-university-collaborate-on-granulocyte-isolation-research

Project experiences (EU / international): COST Action CA15213 (as MC member)
Research Interests: Femtoscopy encompasses methods that enable one to measure femtometer-scale geometries. In heavy-ion physics, this is achieved through the investigation of quantum-statistical correlations among the observed particles. The system's size, lifetime, geometrical as well as flow asymmetry can be studied, which, coupled with phenomenological models, carry information about the Equation of State of the matter. Furthermore, the strength of the multi-particle correlations reveals the ratio of primordial particles to decay products, has connections to in-medium mass modifications, symmetry restorations, and may give insight on possible exotic particle emitting sources such as coherent hadron production or pion lasers. Our group spearheads the effort to establish the methodology of novel types of femtoscopic measurements utilizing Lévy distributions to describe the system. We publish phenomenology and experimental papers on the topic at SPS (NA61), RHIC (PHENIX and STAR) and LHC (CMS). We welcome postdoctoral candidates interested in joining this endeavor, from the experimental (NA61, PHENIX, STAR or CMS) and/or the phenomenological side.
ORCID: https://orcid.org/0000-0002-3154-6925
Contact e-mail: csanad@elte.hu
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Name of the host institution: Budapest University of Technology and Economics
Faculty: Faculty of Chemical Technology and Biotechnology
Name of the supervisor: Péter Nagy
Organisational Unit / Research Group: ERC Molecular Quantum Simulation Research Group
Research Team: Team members: PI, 4 postdoc, 3 PhD, 3 undergraduate:
Expertise: development of quantum chemistry models and programs with universally trusted predictive power and their acceleration to routinely affordable simulation times. Modeling and understanding of complicated molecular interactions and reactions that are not accessible accurately without our models, including drug-protein, supramolecules, molecular crystals, and reactivity across homogeneous, surface and enzyme catalysis.
Project experiences (EU / international):
European Research Council (ERC) project 2023 aCCuracy: Turning gold standard quantum chemistry into a routine simulation tool: predictive properties for large molecular systems
MSCA PF project 2025 DNACopyComp: Development of a predictive quantum simulation protocol for understanding DNA polymerase function and toxic inhibition
MSCA cofund project 2025: Development of a broadly predictive quantum model applicable to practical transition-metal catalysis: understanding CO2 reduction
MSCA cofund project 2026: Harnessing gold-standard predictive power for solid-state batteries: Optimization of the conductivity and polymerization catalysts for polymer electrolytes
Research Interests: Predictive modeling and atomistic understanding of challenging covalent- and non-covalent interactions in large molecules using our models, where modern workhorse computational methods (such as DFT) have well-known difficulties:

1. complicated interactions between large molecules governing, e.g., supramolecular and catalyst-substrate interactions. E.g., protein-drug interactions and molecular crsytals.
2. the mechanism of environment-friendly and selective organo-, and earth-abundant transition-metal catalytic reactions. E.g., CO2, H2O, and N2 activation.
3. surface and enzyme catalysis in proper solvent, crystal, and protein environment. E.g., computational mechanistic study of phosphate reactions relevant to cancer formation or human DNA and viral RNA polymerization and related drug design
   
   Development of quantum chemistry models and programs:
4. improving the accuracy and speed of our methods via better approximations (via, e.g., higher-order perturbative estimates, explicit electron correlation, improved long-range interactions, etc.) and a massively parallel code
5. development and practical implementation of similarly efficient DFT and local CCSD(T) level observables, such as thermodynamic, structural, spectroscopic, and dynamic molecular properties
6. Further development and application of our multilevel or embedding methods using gold standard accuracy for the chemically active region combined with cost-efficient models (MP2, DFT, ML, MM) to take into account biochemical, crystal, and solvent environment effects.
7. Blending wavefunction and DFT methods to extend and improve our double-hybrid and wavefunction-in-DFT embedding approaches
8. Extensions of these efficient and accurate DFT, PT, and CC methods to open-shell and multireference systems.

Detailed project description: www.fkt.bme.hu/~theoreticalchem/index.php/open-positions
ORCID: orcid.org/0000-0001-6692-0879
Contact e-mail: nagy.peter@vbk.bme.hu
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Name of the host institution: ELTE Eötvös Loránd University
Faculty: Faculty of Science
Name of the supervisor: Eszter Ari, PhD
Organisational Unit / Research Group: Institute of Biology, Department of Genetics, Evolutionary Bioinformatics Research Group
Research Team

• Eszter Ari, associate professor, group leader
• Ágoston Hunya, graduate student
• András Asbóth, graduate student
• Csenge Gazsó, master student
• Norbert Kada, master student

Research interests:

• evolution
• bioinformatics
• phylogenetics
• microbiology
• transcriptional regulation
• CHIP-seq, ATAC-Seq

Project experiences: (EU / international):
International project experiences:

• szter Ari was a supervisor of a Visegrad 4 Eastern Partnership (V4Eap) 2014 – 2015 grant project, connected to the fellowship of Zeljko Popovic; Title: DORMANCYbase – developing a database on gene and protein expression during dormancy in animals; 3,000 EUR

National project experiences:

• Supported Research Groups Programme 2025 - 2028: Roles: Eszter Ari - co-applicant, Ágoston Hunya - researcher; Title: Genomic surveillance for precision therapies against antibiotic-resistant bacteria; PI: Papp Balázs; Number: TKCS-2024/66; amount awarded to ELTE: 30,000,000 HUF
• ELTE international online courses 2025: the “Analysis of OMICS Data PR” was selected as a course in CHARM-EU; 500 000 HUF
• Hungarian National Research Fund Grant – Postdoctoral Excellence Programme 2020 - 2023: Eszter Ari’s role: Principal investigator; Title: How do ‘superbugs’ emerge? - A systematic study of the mobility of resistance and virulence genes in human microbiota and pathogenic bacteria; Nr. 131839; 25,500,000 HUF

Research Interests:
Research topics:

• Investigating the mobility of antimicrobial resistance and virulence genes: Horizontal gene transfer between bacterial lineages is widespread and plays a key role in the evolution of antimicrobial resistance and virulence. Despite its clinical importance, however, we have only a limited understanding of (i) the general trends and impacts of gene exchange between virulent pathogens and multidrug-resistant commensal bacteria. We address these issues by analyzing the gene exchange networks of human microbiota, multidrug-resistant and pathogenic bacteria alike. We have published our previous works in Cell and Nature Microbiology.
• Creating and maintaining the TFLink database, an integrated gateway to access transcription factor - target gene interactions for multiple species: We created and maintain the TFLink database that uniquely provides comprehensive and highly accurate information on transcription factor - target gene interactions, nucleotide sequences and genomic locations of transcription factor binding sites for human and six model organisms. We integrated the results of small- and large-scale approaches from ten different databases. We are working on to make the database organ, tissue, and cell specific using data obtained by various high-throughput methods. We published our results in the Database journal.
• Developing python and R packages in the field of OMICS studies, epidemiology and phylogenetics: mulea, treepruner

ORCID: https://orcid.org/0000-0001-7774-1067
Contact e-mail: arieszter@ttk.elte.hu
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Name of the host institution: ELTE Eötvös Loránd University
Faculty: Faculty of Informatics
Name of the supervisor: Tamás Orosz, PhD, Habil, Dr. Tamás Orosz
Organisational Unit / Research Group: BPIE Research Group – Business Process, Data Science & Intelligent Enterprise Systems
Research Team:
Tamás Orosz (Associate Professor, Group Leader; ERP, digital transformation, AI-driven enterprise systems)
Attila Márton Putnoki (PhD Candidate; cognitive information systems, decision support, HCI)
Arafat Md Easin (PhD Candidate; LLM integration, intelligent automation, generative models)
Georgina Asuah (PhD Candidate; SAP machine learning, custom API integration, analytics)
Imre Munkácsi (PhD Candidate; ERP implementation, Industry 4.0, clean-core systems)
Attila Selmeci (PhD Candidate; GUI development, version control, SOA in ERP)
Dominik Banka (PhD Student; explainable AI, low-code enterprise applications)
Kawkab Bouressace (PhD Student; IoT, real-time data quality monitoring)
Bochra Jendoubi (PhD Student)
Ons Saadallah (PhD Student)
+ 11 MSc students of the Data Science master's degree specialization
Project experiences (EU / international):
DATA-EDIH (European Digital Innovation Hub) – Supporting digital transformation of enterprises through AI, data analytics, and advanced technologies
SAP Manufacturing Execution & Industry 4.0, 2022
Agricultural SAP implementation, 2023
1st SAP UA Community Conference: Central and Eastern Europe, Budapest, 2024 – Organized and hosted by the research group at ELTE Faculty of Informatics
Research Interests:
Key Research Focus Areas:
Agentic AI & LLM Reasoning: Researching agent-based Artificial Intelligence and the use of Large Language Models to provide reasoning-driven guidance in complex decision-making processes.
Adaptive AutoML & Time-Series Forecasting: Developing interpretable hyperparameter optimization frameworks and drift-aware feature engineering to enhance forecasting robustness in dynamic environments (e.g., energy, finance).
List of the Research Interests:

• Generative Models and LLM Integration
• Advanced Analytics & Predictive Modeling
• Natural Language Understanding
• Sustainability & Innovation in ERP
• In Memory Database Architectures
• Enterprise Architecture Best Practices
• Commercial & Open Source Technology Stacks
• Big Data, Machine Learning, and IoT in Enterprise Contexts
• Smart Automation & Intelligent Orchestration
• Cloud Native and DevOps Driven ERP Deployment
• Cognitive Information Systems & HCI
• AI and BI Powered Enterprise Functions
• End to End Digital Transformation
• Explainable AI and Low-code technologies in ERP systems

ORCID: https://orcid.org/0000-0002-3154-6925
Contact e-mail: orosztamas@inf.elte.hu
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Name of the host institution: Budapest University of Technology and Economics
Faculty: Faculty of Electrical Engineering and Informatics
Name of the supervisor: Dr. Bálint Hartmann
Organisational Unit / Research Group: Department of Electric Power Engineering, BME FASTER Research Group
Research Team: The head of the Research Group is Bálint Hartmann, senior research fellow, deputy-head of department. He received his M.Sc. degree, Ph.D. degree and habilitation from Budapest University of Economics (BME) in 2008, 2013 and 2022, respectively. His early research focused on the role of energy storage in the power system and the integration of variable renewable energy sources. Most recently, his interest was in the topological properties, the evolutionary processes of transmission and distribution networks, and how the previous affect the vulnerability of the system. He is the author of 111 journal and 92 conference papers, 11 books or book chapters and has received 1023 independent citations for his publications. His cumulative impact factor is 189.257.
István Vokony (M.Sc. 2007, Ph.D. 2012 @ BME), associate professor, head of Department. His primary area of expertise lies in power system stability, renewable energy integration, distributed energy generation, smart grids and energy policy. He also explores the corporate models within the sustainable electricity industry. 
Péter Sőrés (M.Sc. 2013 @ BME), assistant lecturer. His profession includes complex, cross-business business analyses and he translates new solutions into practical projects with his team. His research curriculum includes energy market design, market operation algorithms and the allocation of transmission capacity, demonstrated in academic papers and R+D+I projects.
Project experiences (EU / international):
Selected projects of the group

• RECORD - Reimagining the EleCtric pOwer gRiD, 2025-2028, National Research Excellence Programme ADVANCED_24, Hungarian Academy of Sciences, € 300k
• HYNET: 2024-2027, HORIZON-CL5-2024-D3-01 (Grant No: 101172757), € 238k
• TwinEU: 2023-2026, H2020-EU.3.3.4., € 1,653k
• Budapest CARES: 2023-2025, H2020 Net Zero Cities, € 204k
• OneNet: 2020-2023, H2020-EU.3.3.4. (Grant No: 957739), € 1,017k, Demonstration leader
• FARCOSS: 2019-2023, H2020-EU.3.3.4. (Grant No: 864274), € 683k, Work Package leader (WP8)
• Lendület Grant: 2019-2022, Hungarian Academy of Sciences, € 400k, Principal Investigator
• INTERRFACE: 2019-2022, H2020-EU.3.3.4. (Grant No: 824330), € 995k, Task leader
• FLEXITRANSTORE: 2017-2022, H2020-EU.3.3.4. (Grant No: 774407), € 674k, Work Package leader (WP9)

Research Interests: The BME FASTER Research group deals with the modeling of electricity networks, increasing their stability and reliability, supporting long-term planning and operational management with applied scientific methods.

• We shape the future of power grids through science-based insights and practical solutions – for stable, reliable, and sustainable operation.
• By developing innovative methods, we contribute to the renewal of power grids, offering both theoretical and practical support to professionals and the industry.
• We are one of Hungary’s leading research teams in the field of electric power systems – delivering scientific excellence, practical solutions, and business reliability in a strategically vital domain that affects everyone.

The RECORD (Reimagining the EleCtric pOwer gRiD) project aims to develop a unified theoretical framework to address these challenges by integrating infrastructure planning, power system operations and control, and electricity markets. This framework will ensure interoperability across different timeframes and spatial resolutions, enhancing our understanding of future power system behaviour and vulnerabilities. The three will explore timely questions of failure cascades, grid flexibility, AI-based decision-making, new control parameters, NP-hard problems in electricity markets, scalable optimization models and more. This research shifts the focus from short-term, simplistic solutions to a long-term, holistic approach addressing the multifaceted challenges of modern power grids. With over 40 research and consultancy studies conducted in the last five years, the team of the RECORD project builds on extensive experience to offer innovative solutions and set new benchmarks in energy research, ultimately contributing to a more resilient, efficient, and sustainable power system.
ORCID: 0000-0001-5271-2681
Contact e-mail: hartmann.balint@vik.bme.hu
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