Call for Postdoctoral Researchers, Research Assistants and Research Interns
We, at the Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST) - Tokyo, are looking for postdoctoral researchers, research assistants and research interns. The candidate is expected to work in one of the following directions:
1) Scalable and distributed graph processing / dynamic graphs / stream processing of graphs.
The applicant is expected to work on the research and development related to distributed, scalable and real-time graph processing. A background in graph processing and big data processing platforms is desirable.
2) Big spatial data / real-time data integration and processing.
The applicant is expected to work on the research and development related to distributed and scalable spatial data processing using state of the art big data processing platforms. A background in big data processing platforms is desirable.
3) Graph analysis and graph learning, with emphasis on accelerating, scaling up these technologies and applying them to large-scale data.
The topics include but not limited to: (a) Graph neural network (GNN); (b) Graph embedding; (c) Graph analysis community detection, graph/node classification, link prediction, node ranking, centrality); (d) Knowledge Graph (KG); (e) Temporal-spatial graphs; (f) Fairness, interpretability and explainability for graph learning; (g) Differentially private analysis of graphs; (h) Federated graph machine learning; (i) AutoML for graphs; (j) Various types of graphs (bipartite graph, multi-layer graphs, signed graphs, heterogeneous graphs); (k) large-scale graph visualization; (l) Applications of these technologies, such as recommender systems, point clouds, anomaly detection, text classification, image classification, sentiment analysis, question answering, emergency behaviors modeling and prediction, drug discovery, social influence prediction, traffic forecasting, neural machine translation, etc.
Desired candidate attributes (satisfying a few of the attributes is enough):
- Database concepts
- Knowledge of distributed and parallel computing
- Knowledge of machine learning / deep learning / graph learning / data mining
- Good coding skills in java and/or python
- Experience of version control system
- Strong analytical and problem-solving skills
Interested candidates may send their resume to the following email address: shaikh.salman(a)aist.go.jp
AIRC homepage: https://www.airc.aist.go.jp/en/teams/
Thank you very much.
Best Regards,
---
Salman Ahmed Shaikh (Ph.D.)
Senior Researcher
Artificial Intelligence Research Center (AIRC)
National Institute of Advanced Industrial Science and Technology (AIST)
Tokyo Waterfront 2-4-7 Aomi, Koto-ku, Tokyo 135-0064 JAPAN
Email: shaikh.salman(a)aist.go.jp
Call for Postdoctoral Researchers, Research Assistants and Research Interns
We, at the Artificial Intelligence Research Center (AIRC), National Institute of Advanced Industrial Science and Technology (AIST) - Tokyo, are looking for postdoctoral researchers, research assistants and research interns. The candidate is expected to work in one of the following directions:
1) Scalable and distributed graph processing / dynamic graphs / stream processing of graphs.
The applicant is expected to work on the research and development related to distributed, scalable and real-time graph processing. A background in graph processing and big data processing platforms is desirable.
2) Robust AI by Integration of Knowledge Representation and Machine Learning.
The applicant is expected to work on the development of innovative fundamental technologies that combine symbolic reasoning and statistical learning. A background in Artificial Intelligence is expected, in at least one of the two areas of Knowledge Representation and Reasoning, and/or Machine Learning. Themes of research may include but are not restricted to: robust / explainable AI, opinion diffusion, belief change, fact checking, logic programming.
3) Big spatial data / real-time data integration and processing.
The applicant is expected to work on the research and development related to distributed and scalable spatial data processing using state of the art big data processing platforms. A background in big data processing platforms is desirable.
4) Graph analysis and graph learning, with emphasis on accelerating, scaling up these technologies and applying them to large-scale data.
The topics include but not limited to: (a) Graph neural network (GNN); (b) Graph embedding; (c) Graph analysis community detection, graph/node classification, link prediction, node ranking, centrality); (d) Knowledge Graph (KG); (e) Temporal-spatial graphs; (f) Fairness, interpretability and explainability for graph learning; (g) Differentially private analysis of graphs; (h) Federated graph machine learning; (i) AutoML for graphs; (j) Various types of graphs (bipartite graph, multi-layer graphs, signed graphs, heterogeneous graphs); (k) large-scale graph visualization; (l) Applications of these technologies, such as recommender systems, point clouds, anomaly detection, text classification, image classification, sentiment analysis, question answering, emergency behaviors modeling and prediction, drug discovery, social influence prediction, traffic forecasting, neural machine translation, etc.
Desired candidate attributes (satisfying a few of the attributes is enough):
- Database concepts
- Knowledge of distributed and parallel computing
- Knowledge of machine learning / deep learning / graph learning / data mining
- Good coding skills in java and/or python
- Experience of version control system
- Strong analytical and problem-solving skills
Interested candidates may send their resume to the following email address: shaikh.salman(a)aist.go.jp
AIRC homepage: https://www.airc.aist.go.jp/en/teams/
Thank you very much.
Best Regards,
---
Salman Ahmed Shaikh (Ph.D.)
Senior Researcher
Artificial Intelligence Research Center (AIRC)
National Institute of Advanced Industrial Science and Technology (AIST)
Tokyo Waterfront 2-4-7 Aomi, Koto-ku, Tokyo 135-0064 JAPAN
Email: shaikh.salman(a)aist.go.jp
日本データベース学会の皆さま 早稲田大学の酒井と申します。
情報検索国際会議のトップカンファレンスSIGIRの
Asia-Pacific版、SIGIR-AP (Asia-Pacific) が立ち上がります。
記念すべき第一回は今年11月に北京でハイブリッド開催されます。
SIGIRで不採録となった論文も復活できるチャンスがあります。
詳しくは下記CFPとホームページをご覧ください。(一部under constructionです。。。)
ご投稿をお待ちしております。
SIGIR-AP 2023 First Call For Papers
Details: http://www.sigir-ap.org/sigir-ap-2023/call-for-papers/
SIGIR-AP (Asia/Pacific) is a new regional IR conference whose scope is the same as that of SIGIR. It will be hybrid, so authors of accepted papers can either present their work in-person or remotely.
The conference adopts double-blind, single-track reviewing, and allows submissions of papers that are commensurate with contribution size. There are two types of SIGIR-AP submissions: Regular submissions and SIGIR-Revise-and-Resubmit submissions.
Regular submissions:
These are new, original contributions that have not been submitted elsewhere before. We also welcome Resource papers and Reproducibility papers. Resources papers consider test collections and labelled datasets, designs and protocols of evaluation tasks, and software tools and services for information access. Reproducibility papers repeat, reproduce, generalize, and reexamine prior work, for example analysing to what extent assumptions of the original work are valid, or identify error modes and unexpected conclusions; typically these papers involve a new team and a new experimental setup, that is, they go beyond replication.
Page length: (A) Paper body + (B) references.
(A) Paper body length should be 2-9 pages, and be commensurate with contribution size.
(B) No page limit for the references section.
SIGIR-Revise-and-Resubmit (SIGIR-RR) submissions:
These are revised versions of either full or short papers that were not accepted at the immediately preceding SIGIR conference. (This option is not available for other SIGIR paper tracks, e.g., perspectives, resource, or reproducibility.) In addition to the revised paper, the authors must include in the submission file a text explaining the revision based on the SIGIR reviews, as well as the original SIGIR submission.
Page length: (A) Paper body + (B) references + (C) explanation (with SIGIR paper ID) + (D) SIGIR submission
(A) Paper body length should be 2-9 pages, and be commensurate with contribution size.
(B) No page limit for the references section.
(C) Explanation (1-3 pages, no style requirements): a SIGIR paper ID, plus a text that explain how the authors addressed the points raised by the SIGIR reviewers after the SIGIR rejection.
(D) SIGIR submission: please include the original anonymised SIGIR submission as is in the SIGIR-AP submission.
IMPORTANT DATES (Timezone: Anywhere on Earth)
June 26, 2023 Abstract submissions due
July 3, 2023 Paper submissions due
September 10, 2023 Paper decision notifications
September 26, 2023 Camera ready papers due
November 26-29, 2023 SIGIR-AP conference (tutorial2 + 2-day conference + workshops)
PROGRAM CHAIRS
Xuanjing Huang, Fudan University, P. R. China
Tetsuya Sakai, Waseda University, Japan
Justin Zobel, University of Melbourne, Australia
sigirap2023pcchairs(a)easychair.org