The University of Primorska (UP) is a medium-sized, internationally engaged university with 5,745 students, of whom 1,122 (or 19.5%) come from 39 foreign countries. It has 730 staff. The UP has 480 researchers and higher education teachers, of whom 59 (or 12.3%) come from abroad. The UP hosts more than 200 international researchers every year and enrols 190 PhD students.

UP carries out 79 study programmes in all three cycles, 16 of which are also held in the English language. In addition, the UP organises from 5 to 10 international summer schools on various topics every year as well as over 10 international scientific conferences, including at least a major conference, as it has done for several consecutive years, with more than 1,000 participants and under the patronage of the President of the Republic of Slovenia.

The Faculty of Mathematics, Natural Sciences and Information Technologies (UP FAMNIT) is a member of the University of Primorska and was founded in 2006. A total of 1.035 students are enrolled in study programmes for the 2023/24 academic year, of which 768 were in undergraduate programmes, 215 in master’s programmes, and 52 are enrolled in doctoral programmes. 

The research group of Assistant Professor Aleksandar Tošić is currently looking for a Doctoral researcher (PhD candidate) in Computer Science in collaboration with Innorenew CoE. The expected start date is 1st October 2024.

Your role

The successful applicant will be employed at the Andrej Marušič Institute, a PhD candidate at UP FAMNIT’s Computer Science programme, and a member of the Information Processing group at InnoRenew CoE. The proposed PhD position will be a continuation of research on the topic of network analysis done by Dr. Aleksandar Tošić, and Dr. Niki Hrovatin. The unique collaboration with InnoRenew CoE is an opportunity to shape a fruitfully interdisciplinary research with other research groups. The candidates role is to identify, understand and finally model problems in other domains as graphs/network. Commonly such graphs are large and require careful considerations in terms of storage as well as computation. Parallel and Distributed computing plays a significant role in solving such problems in practice. The candidate will design software solutions that make use of the University’s High Performance Computing (HPC) cluster to solve these problems, analyze and learn from the networks observed.

Your Mission

• Research on the existing results on different techniques of graph encoding/storage, and network analysis algorithms
• Disseminate results through scientific publications and talks at conferences
• Obtain a PhD in Computer Science
• Support for teaching of up to 4h per week
• Work on ongoing and future research and industrial projects

What we expect from you

Master-degree in Computer Science or Applied Mathematics.

• Programming skills are required. Ideally, candidates should be experienced in one or more of the following programming languages: Java, C++, Rust, Kotlin, typescript, and R.
• Knowledge on data structures and algorithms is required. Candidates should be able to identify the ideal graph representation and algorithms for performing specific analysis on graph data.
• Proven interest in interdisciplinary research, graph theory and network analysis.
• Commitment, team spirit, a critical mind, and motivation are skills that are more than welcome.
• Language: English is a must, Slovenian would be an asset.
• Fulfill the requirements for doctoral students at the University of Primorska.
• For more details, see: https://www.famnit.upr.si/en/education/doctoral/computer-science/#heading5
• Planned research work

Analysis of Large Transactional Data:

Decentralized networks like Bitcoin and Ethereum are in essence an immutable record of transactions, or, in the context of smart contracts, as logs of state transitions. Due to their vast user base and applications, the transactional data of these networks hold important insights for business anlytics, market trends, consumer behavior as well as offering a detailed perspective on the networks’ operational dynamics. However, these data are typically an untapped asset due to the vastness and difficulties in processing.

Our goal is to develop new methods and approaches to extract relevant information by applying graph theory concepts and using high-performance computing infrastructure. For context, we include a link to recent research that will be further investigated and built upon during the candidate’s study period.

Analysis of mycelial networks:

Mycelial fungi grow as indeterminate adaptive networks that have to forage for scarce resources in an unpredictable environment. Development of contrast-independent network extraction algorithms has dramatically improved our ability to characterise these dynamic macroscopic networks and promises to bridge the gap between experiments in realistic experimental microcosms and graph-theoretic network analysis, greatly facilitating quantitative description of their complex behaviour. The research efforts can be split into designing algorithms for network reconstruction from microscopy images, and analysis of obtained networks in order to understand the intricate relationships between growth and the environment.

Collaboration networks:

In science, collaboration is an important aspect in knowledge transfer and co-creation. Collaborations are mainly observed through co-authoring scientific publications. The underlying network of collaborations has been studied extensively. However, these networks are less often enriched with other data sources i.e. funding, affiliations, title, etc. In an effort to observe interesting patterns, identify anomalies, potential threats and success stories, a collaboration network was built and enriched with other data sources. Analysis of these large networks may offer new insights into the effectiveness of existing research funding models. Additionally, discrete event simulation can be built by observing collaboration networks in order to test and validated different approaches to a sustainable and fruitful funding model for science.

How to apply

• The applicants should send:
• Curriculum vitae (including short descriptions of all prior educations, qualifications, and research or work experiences)
• Motivation letter for this doctoral position (maximum one A4 page)
• Github/Gitlab link
• List of publications (if applicable)

to this email address: vanessa.pobega@famnit.upr.si. In the title (subject) of your email please write: PhD Complex Network Analysis. All materials should be in English and submitted in PDF format no later than 15th of June 2024.

Review of applications will begin immediately and continue until the position is filled. For further information about the research topic, please contact Assistant Professor Aleksandar Tosić at the email address: aleksandar.tosic@upr.si.

The University of Primorska reserves the right for justified reasons to leave the position open, extend the application period, and consider candidates who have not submitted applications during the application period.