PhD Stipends in Graph Data Management, Mining, Systems, and Machine Learning on Graphs (16-22036)

PhD Stipends in Graph Data Management, Mining, Systems, and Machine Learning on Graphs (16-22036)

At the Technical Faculty of IT and Design, Department of Computer Science, Aalborg University, two fully-funded PhD scholarships are available, commencing on September 1, 2022 or shortly thereafter. The Department of Computer Science features a broad range of synergistic activities within research and education in the general area of computer science, including curiosity-driven research and targeted research in collaboration with industrial partners, as well as traditional university education, with a unique problem- and project-based focus, and continued education and knowledge dissemination. For more information about the department see: https://www.cs.aau.dk/about

Job description

The PhD students will be working on the broad area of graph data management, mining, systems, and machine learning on graphs. The project is funded by a Novo Nordisk Foundation RECRUIT grant ("Data Management, Algorithms, & Machine Learning for Emerging Problems in Large Networks – with Interdisciplinary Applications in Life & Health Sciences". NNF22OC0072415).

Human-in-the-loop Data Mining and Deep Learning on Graph Data: Graph data, e.g., social and biological networks, financial transactions, transportation systems, and telecommunication networks are pervasive in the natural world, where nodes are entities with features and edges represent relations among them. Machine learning and deep learning over graphs become ubiquitous, for instance, in cheminformatics (drug discovery, designing molecular structures with desired properties, virtual screening) and bioinformatics (drug-disease association and protein interaction prediction), detectingmalwares and abnormal transactions, classifying customers based on calling behavior, feeds on Twitter, Facebook, and churn prediction. Despite deep learning models often achieving state-of-the-art performance in many tasks, they are “black-box”: It is difficult to understand which aspects of the input graph data drive the decisions of the model. Interpretability can improve the model's transparency related to fairness, privacy, and other safety challenges, thus enhancing the trust in decision-critical applications and ease their adoption in life science, health, law enforcement, and financial domains. To this aim, we shall design a user-in-the-loop interpretation framework that translates deep learningbased findings back to users, supports “why” and “why-not” questions over prediction results (e.g., “why a new app is classified as a malware”? Or “what minimum, valid changes in a molecule structure would optimize desired chemical and biological properties”?), assists users in formulating relevant questions with minimum efforts, and incorporate users’ interactive feedback to improve training data and deep learning models.
The aim will be publishing several research papers at top-tier data mining (KDD), data management (SIGMOD, PVLDB, ICDE), or machine learning (NeurIPS, AAAI) conferences based on the research works. Within the area, the position comes with many freedoms in terms of the specific research direction, methodology, and approaches taking the specific project needs into consideration.
The selected candidates will be part of the Data Engineering, Science and Systems (DESS) research group, an ambitious and supportive research environment in which we study data engineering, data science, and data systems. Embracing the opportunities enabled by the ongoing, sweeping digitalization of societal, industrial, and scientific processes, we collaborate with our partners to create value from data, targeting the invention of purposeful artifacts, such as frameworks, algorithms, data structures, indexes, languages, and techniques, as well as tools, systems, and prototype software, typically either for proof-of-concept or for the purpose of conducting empirical studies.
Requirements:
The applicants must have a master’s degree in computer science, data science, artificial intelligence, or a closely related field. Due to the project’s interdisciplinary angle, the applicant must have a strong background in algorithm design, machine learning, and software development. Outstanding spoken and written communication skills in English are essential.
Application:
The application must contain:

A cover letter of max. 1 page, including (i) motivation for applying, (ii) preferred starting date (specifically if other than 1 September 2022), and (iii) a brief explanation of the applicant’s background.

A research statement (project description) roughly within the frame of ‘Graph Data Management, Mining, and Machine Learning on Graphs’ of max. 2 pages (excl. references). This description should outline the applicant’s thoughts and ideas on possible research directions within the context of the topic outlined above.

CV

Diploma and transcripts of records

Contact of two references

Other relevant information

Interested applicants are encouraged to contact the project’s principal supervisor, Associate Professor Arijit Khan, Department of Computer Science, e-mail: [email protected] , regarding the scientific aspects of the position. His research works can be found at https://scholar.google.com/citations?user=6Ym8k4AAAAJ&hl=en and at https://dblp.org/pid/67/2933.html .
PhD stipends are allocated to individuals who hold a Master's degree. PhD stipends are normally for a period of 3 years. It is a prerequisite for allocation of the stipend that the candidate will be enrolled as a PhD student at the Technical Doctoral School of IT and Design in accordance with the regulations of Ministerial Order No. 1039 of August 27, 2013 on the PhD Programme at the Universities and Certain Higher Artistic Educational Institutions. According to the Ministerial Order, the progress of the PhD student shall be assessed at regular points in time.

Shortlisting:
Shortlisting will be applied. This means that subsequent to the deadline for applications the head of department supported by the chair of the assessment committee will select candidates for assessment. All applicants will be informed whether they will be assessed or not.
For further information about stipends and salary as well as practical issues concerning the application procedure contact Ms. Sofie Pia Jensen, The Doctoral School at The Technical Faculty of IT and Design, email: [email protected] , phone: +45 9940 3478.
For more information of The Technical Doctoral School of IT and Design: www.phd.tech.aau.dk

The application is only to be submitted online by using the "Apply online" button below.
AAU wishes to reflect the diversity of society and welcomes applications from all qualified candidates regardless of personal background or belief.

Agreement

Appointment and salary as a PhD fellow are according to the Ministry of Finance Circular of 28 June 2019 on the Collective Agreement for Academics in Denmark, Appendix 5, regarding PhD fellows, and with the current Circular of 11 December 2019 on the employment structure at Danish universities.

Vacancy number

16-22036

Deadline

15/06/2022

Apply online
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