- Consultancy on industry direction and academic research scope. Help formulate and focus research and development direction in leveraging knowledge graph and graph analytics for solving business problems such as recommendation, fraud detection in advertising/e-commerce area.
- Consultancy in algorithm review and system enhancement for machine learning platform and the modules which may lead to patentable results on graph analytics/pattern discovery;
- Provide project service, help to solve the technique issues encountered during the project and suggest potential performance benchmark model and methodology for improvement;
- Bi-weekly meeting with the team.
- 6 or more technical lectures given related:
- Latest technical trend in knowledge graph and graph analytics.
- Motif-based, Meta-path based graph analysis and pattern discovery
- Graph based recommender system trend and future direction.
- Uncertain data modelling, queries, and analysis
- Efficient Query Algorithms for Uncertain Graph Databases.
- Offer guidance and advice to problem formulation and algorithm design, for adverting graph fraud detection analytics models and graph-based recommendation models and algorithms;
- Review the algorithm models and results, instruct on the cause of the problem and performance improvement solution;
- Contribute innovative ideas in the field of graph analytics for
- Fraud detection/ pattern discovery.
- Recommendation/targeted marketing.
- PhD. Degree in Computer science or related disciplines;
- 10+ years of academic research experience in database, data management and/or big data analytics is highly preferred.
- Strong publication in world top conferences/journals in database and data mining such as SIGMOD, VLDB, ICDE, KDD, IEEE TKDE, VLDBJ, etc is highly preferred;
- International recognition in research community, as evident by holding important positions (e.g., associate editor in top journals, steering committee members, PC chairs, area chairs, or PC members in top conferences) is highly preferred.
- Strong mathematical modelling and performance analysis capability in graph data is highly preferred;
- Broad knowledge and connection in the community – keep abreast of the state of art of the latest progress, other industry players approach and academic R&D direction is highly preferred.
- Good understanding and appreciation of algorithmic level fine tuning versus system-level performance optimization – various tradeoff, parameters, adjustment, and modeling effects etc. is highly preferred.
Interested candidates please send application (quoting Ref. No.) with detailed resume, current and expected salary to Talent Acquisition via email to [email protected]
Only short-listed candidates will be notified. ASTRI reserves the right not to fill the position.
ASTRI is an Equal Opportunities Employer. Personal data provided by job applicants will be used exclusively for recruitment only.