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Jobs Posted on the Whova Community Board of The 22nd International Semantic Web Conference (ISWC 2023)

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Senior Technical Consultant - Knowledge Graph Technologies
metaphacts GmbH
You work with our sales and project management organization and realize customer projects to make them a success. You follow agile project principles (Scrum, Kanban, SAFe), understand customer needs and requirements quickly and can translate them into crisp work packages. You have a technical understanding of the technologies we use (RDF, SPARQL, OWL and HTML, CSS) and apply them to satisfy customer requirements.

Your role also includes the coordination with our product management and engineering team to help translate the requirements which you gather from end users and professionals from all levels of the customer organization, into implementable user stories. You have excellent communication skills (in English) and feel confident in giving project milestone demos and presentations to customers. You take responsibility for customer satisfaction and act as a technical advisor to the customer.
Intern: RS - Foundation Models for Data Management & Lakehouses
This is for a 2024 summer internship with the following start dates: May - August or June - September for quarter system schools.

We are broadly interested in making foundation models (FMs) effective for a range of data management tasks, particularly those related to the management of structured data in enterprise data lakes and lakehouses.

Topics of interest include research on effective and efficient tuning techniques, knowledge-driven reasoning, and causality-driven alignment for better control and run-time performance of FMs and their use in enterprise data tasks. Tasks of interest include semantic enrichment of structured data, semantic data management with metadata and knowledge graphs, code generation for data retrieval with transformations, and various data wangling tasks in the end-to-end data lifecycle in data lakes.

Tuning-related research spans full-space and parameter-efficient tuning techniques with supervised as well as reinforcement learning with reward functions that capture end-use performance. Grounding the generation of tuned models in domain-specific vocabulary, efficient techniques for human-in-the-loop adaptation at inference time, and retrieval augmentation techniques for data management tasks will be of interest.

For knowledge-driven reasoning, formulations and benchmarks that treat the database query-answering process as a knowledge-extraction task will be useful for experimenting with reasoning over database tables at different levels of complexity to improve and expand the reasoning skills of FMs.

For causal alignment, we’re interested in formulations that study and show the causal relationships behind the effectiveness of different prompt optimization methods, where a small set of prompt augmentation tokens improves FMs for issues like delusions, alignment, and transfer.

See the official posting for more details:
PhD student
Bosch AI
Nero Symbolic AI
Link: None
Internships PhD and MSc levels
Bosch AI
Neuro Symbolic AI
Link: None
Research Engineer
Neuro Symbolic AI
Link: None
LLM and semantic data engineer
Work with us on exciting use cases for KG building and using LLMs for NLP, KG building and question answering. Variety of domains, from financial to building regulation to energy and industrial data.
Internship On Job Recommendation From A Heterogeneous Graph
Telecom SudParis
Job recommendation is the task of associating candidates with jobs. This can be useful for candidates who would like to f i nd to best possible jobs, for companies that want to f i nd the rarest talents in the vast pool of candidates, but also for independent recruiters who need to be as precise as possible when they send a resume to a company.
In this internship, you will work on a new dataset for job recommendations.
Its particularity is that it contains much additional information about candi-dates and jobs we can represent as a graph. Besides, it is very sensitive to the cold start problem: We have many new candidates and new jobs, and it restricts a lot of the algorithms we can use.
If we consider video recommendations on Youtube, an average viewer watches many videos, and each video is viewed many times. Therefore, when recom-mending new videos to a specif i c user, we can look at what other similar view-ers watched and recommend the most relevant video. This is the principle of collaborative f i ltering. In our case, our users are likely to get a job and never come back. Likewise, jobs are associated with one person, and then, we are done with it. Therefore, we need to exploit extra information to make the recommendation.
For our dataset, we can represent our pool of candidates and jobs with a heterogeneous graph, connecting candidates and jobs, but also additional node types like skills, cities, or employment types. Because we have this expressive representation, we must adapt the existing algorithms. During the internship, we will see how graph neural networks can be used to make recommendations, and we will propose a new architecture to solve our specif i c problem.
The goal of this internship will be to publish a paper at an international conference. The intern will work together with a Ph.D. student.
Tenure Track Assistant Professor Data Engineering
University of Amsterdam
The Informatics Institute of the University of Amsterdam is seeking an assistant professor in data engineering.  This position will be located in INtelligent Data Engineering Lab ( The lab's broad aim is to tackle problems relating to the preparation, management, integration and reuse of data using empirical insights into data science practice.

You will contribute to the lab through innovative research in topics related to data engineering and data management.

The successful candidate will have a background in data management or in artificial intelligence/machine learning with an orientation towards data management. This should be evidenced by a track record of publications in renowned international conferences and journals. Relevant venues are VLDB, SIGMOD, ICDE, CIDR, CIKM, ISWC, WWW, AKBC.

Areas of interest include (but are not limited to): data management for machine learning, data integration, data cleaning, dataflow systems, responsible data science, automated knowledge base construction, and semi-structured/semantic data management.

As an educator, you will also contribute to the Informatics Institute education program. The program includes bachelor's and masters’ programs in data science, computer science and artificial intelligence. Teaching is approximately 30% of the position’s total time.

Additionally, you will be expected to acquire funding from sources such as the national funding agency NWO, EU funding via H2020 and industry. Finally, contributions to outreach, both in terms of engaging with the media as well as applying the state of the art research tools to applications in society are valued. UvA spinoffs that bring technology to industry and society are highly encouraged as well as working in the open source community.
PhDs and Postodcs in Data Integration, Data Privacy, Data Sharing and more!
University of Southampton
Looking for PhDs and postdocs to join our team at the University of Southampton. You will design, implement, deploy, maintain and evaluate algorithms and software for one or more of the following areas: data integration, exchange and sharing, data validation, data privacy, automating data agreements, privacy-based data sharing, dataset search, graph problems. We do theory and systems. You will write and submit academic papers, technical reports, and project deliverables, and travel to academic conferences and project meetings to present your work and represent the team. I am at the ISWC so send me a message and let’s meet and chat.
Link: None
We are looking for a Researcher who will join our team and contribute to beyond state-of-the-art research projects in AI and semantic technologies, in collaboration with leading European research and academic institutions and industry. If you are a researcher who is passionate about weaving research results into real-life applications, this position is for you!
Tenure-track or tenured position in responsible AI
Centrum Wiskunde & Informatica (CWI)
CWI is the Dutch national research institute for mathematics and computer science. CWI strives to be a diverse and inclusive institute. To achieve balanced representation in leadership positions, we will prioritize applications from women researchers (including identifying as).

We are seeking applications from both early career researchers (as of last year PhD) and highly experienced researchers (comparable to associate or full professor level). Depending on the level of seniority, a tenure track or a tenured position will be offered.

The Human-Centered Data Analytics (HCDA) group of CWI aims to strengthen its research activities in the field of responsible AI. We are looking for talented scientists who are keen to develop their own innovative line of fundamental research related to fairness, transparency, privacy, or ethics of cutting-edge AI technology.

The HCDA group investigates human-centered, responsible AI in the culture and media sectors. Current research topics in the group include: measuring bias and diversity in recommender systems; detecting offensive colonial terminology in knowledge graphs; developing transparent techniques for misinformation detection; predicting political bias of crowd workers; and analysing usage data of digital library users.

We maintain close collaborations with professionals from the culture and media sectors, as well as social scientists and humanities scholars, through the Cultural AI Lab and the AI, Media and Democracy Lab. These interdisciplinary labs provide us with opportunities to work with real data and real-world use cases.

Next to developing an innovative research line, you will explore collaboration opportunities within the research group and/or within the two interdisciplinary labs, acquire funds to grow our research capacity for responsible AI, supervise PhD students and postdocs, and help strengthen the role of CWI as the national institute for computer science and mathematics.
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