PhD Scholarship: Developing a Manufacturing Analytics Cockpit

Share on facebook
Share on google
Share on twitter
Share on linkedin

Project Background:  In the CONFIRM research centre on Smart Manufacturing as well as in Industry 4.0 in general, a Digital Thread connects the data and processes for smarter products, smarter production, and smarter integrated ecosystems. While the tangible goods (products and production lines) are understood as needing a Digital Twin as an executable model, i.e. an in-silico entity on which to virtually explore design, production, quality, and lifetime maintenance, the immaterial goods like software and analytics artefacts are not yet treated on par.

For this new paradigm to enter mainstream, models need to be coupled with automatic transformations, generations, and analyses that take advantage of the formalized knowledge about the immaterial and material entities. This formalized knowledge includes a variety of models together with Domain Specific Languages that use semantic types at their core.

In this project, we build demonstrators of how the new thinking can disrupt the status quo but empower a better understanding, a more efficient organization, and a more automatic management of the many cross-dimensional issues that future connected software and systems will depend upon.

Research Summary: The objective of this specific work is to develop a set of applications that constitute a Manufacturing Analytics Cockpit.  The Cockpit builds on existing analytics tools capable of formally modelling, reasoning, and visualising discrete manufacturing, inventory management, supply chains, operational risk management, and logistics problems. In the Cockpit, these tools will form an “operating system” for manufacturing planning, execution, control, and decision making to support a sense-analyse-act-visualise cycle.

  • Sense level will focus on aspects of data management, sensor data aggregation, and data interoperability;
  • Analyse level considers real-time data analytics, such as time-series classification and prediction;
  • Act level will combine the use of advanced tools in operations research, AI, planning, diagnosis, and recommender systems to generate schedules and advice to key stakeholder users.
  • Visualisation components will generate configurable user-driven visual querying of the underlying data, analytics, and decisions to help interpretation.

Faculty: Science & Engineering: CONFIRM Centre for Smart Manufacturing

Entry Requirements: Essential attributes: Honours undergraduate (level 8) degree, Master’s degree, or equivalent, in a field related to the project, such as computer science, engineering.

We require persons with expertise in information modelling, use of models in software design, ability to program in Java or similar languages, and good interpersonal skills.

Knowledge of logic or knowledge representation formalisms and previous project experience are desirable skills.

Funding/Stipend: SFI PhD Stipend of €18,500 tax free per annum, EU tuition fees covered up to €5,500 p.a., non-EU students may have to pay balance of full fees.

Closing Date: Rolling deadline until position is filled.

Contact & How to apply: Please send your CV/Resume with the ID REF# C-PHD-3, transcript of records, and a cover letter in support of these requirements and including the name of 2 references with full contact information to



We use cookies to give you the best online experience. By agreeing you accept the use of cookies in accordance with our cookie policy.

Privacy Settings

When you visit any web site, it may store or retrieve information on your browser, mostly in the form of cookies. Control your personal Cookie Services here.

These cookies are necessary for the website to function and cannot be switched off in our systems.

In order to use this website we use the following technically required cookies
  • wordpress_test_cookie
  • wordpress_logged_in_
  • wordpress_sec

For perfomance reasons we use Cloudflare as a CDN network. This saves a cookie "__cfduid" to apply security settings on a per-client basis. This cookie is strictly necessary for Cloudflare's security features and cannot be turned off.
  • __cfduid

Decline all Services
Accept all Services