Doctoral Research Assistant – Energy-Efficient Foundation Models

Doctoral Research Assistant – Energy-Efficient Foundation Models (Ghent)

Apply by 17 Nov 2026

About This Opportunity

Ghent University advertises Doctoral Research Assistant – Energy-Efficient Foundation Models (Ghent), focusing on machine learning for energy-efficient foundation models.

Ghent University IDLab advertises this research assistant position in the Faculty of Engineering and Architecture, with at least 50% of working time devoted to a doctoral dissertation developing energy-efficient foundation models. It also includes bachelor-level computer-science teaching in Dutch and departmental academic service. A relevant engineering or computer-science master’s degree is required and candidates with non-EU qualifications may need a NARIC equivalence certificate. Programming skills in Python, C++, C# or Java are assets. Salary follows the AAP3 scale. The initial contract lasts two years with up to two renewals after positive assessments. This is the machine-learning vacancy referenced 30464, rather than the separate autonomous-robot-collaboration post. Academic preparation and entry profile: A master’s degree in engineering, engineering technology, computer science or an accepted equivalent; Dutch proficiency sufficient to teach undergraduate courses is mandatory. Funding and appointment conditions: Salary scale AAP3; salary amount not specified. Who This Opportunity Is Best Suited For: Based on the documented project scope, this opportunity may be particularly suited to applicants whose preparation includes A master’s degree in engineering, engineering technology, computer science or an accepted equivalent; Dutch proficiency sufficient to teach undergraduate courses is mandatory; researchers interested in working with Machine learning, Foundation models, Python, C++, Java. This is an editorial fit assessment, not an admissions determination. Potential Challenges / Who May Find It Less Suitable: Relevant considerations are the specialized technical breadth may require significant preparation; teaching commitments may affect how research time is organized. The exact implications depend on the stated eligibility and appointment terms. Overall Opportunity Assessment: The distinctive focus is machine learning for energy-efficient foundation models. Applicants should weigh the verified research methods, qualifications and any stated funding or mobility conditions against their research goals.

Machine learning for energy-efficient foundation models

Level PhD

Field Machine learning for energy-efficient foundation models

Organization Type University

Funding & Compensation

Amount Paid

Funding Type Salary

Funding Status Paid

Requirements

Education

A master’s degree in engineering, engineering technology, computer science or an accepted equivalent; Dutch proficiency sufficient to teach undergraduate courses is mandatory.