Researcher in Physics-Informed AI and Mould Design Optimization
Deadline: 15.05.2026
Publisert
Universitetet i Agder har over 1500 ansatte og nesten 14 000 studenter. Det gjør oss til en av Sørlandets største arbeidsplasser. Våre ansatte forsker, underviser og formidler kunnskap fra alle sine fagområder.
Universitetet holder til på to moderne campuser i Kristiansand og Grimstad.
About the position
A fixed-term 100% position is available at the University of Agder (UiA), Faculty of Engineering and Science, as a Researcher in Physics-Informed AI and Mould Design Optimization, affiliated to the Department of ICT, for a period of 2 years. The position is located at Campus Grimstad.
The starting date is expected to be 1 September 2026.
The position is part of the CASTiNET – CASting Through iNtelligent, Enhanced Technologies innovation project, a four-year innovation project (2025–2029) funded by the Research Council of Norway. UiA is collaborating with Institutt for energiteknikk (IFE) and Aludyne Norway AS. The project aims to develop an AI-driven simulation framework for low-pressure die casting of large aluminium automotive structures. It integrates physics-based modelling, machine learning, and real-time production data to significantly reduce simulation time, improve dimensional accuracy, and enable defect forecasting in manufacturing.
Responsibilities
The successful candidate will:
Develop and implement Neural Architecture Search (NAS) frameworks for Physics-Informed Neural Networks (PINNs)
Design and evaluate adaptive sampling strategies for improved physical modelling accuracy
Develop an integrated methodology combining topology optimization and advanced casting simulations
Contribute to software implementation and benchmarking against high-fidelity FEM-based models
Collaborate closely with industrial and research partners to ensure real-world applicability
Required qualifications
PhD (or near completion) in Artificial Intelligence, Applied Mathematics, Computer Engineering, Mechanical Engineering, or a related field. Exceptionally well-qualified applicants with a Master’s degree or this year’s candidates/candidates who will finish their master’s degree in the summer can also apply.
Strong background in physics-informed machine learning and deep learning
Experience with PDE-based modelling, numerical simulation, FEM, or computational mechanics
Programming proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow)
Desired qualifications
Familiarity with optimization methods (e.g., Bayesian optimization, multi-objective optimization)
Experience with topology optimization or computational design optimization
Interest in industrial AI applications and interdisciplinary collaboration
Strong communication skills in English, both written and oral
It is hoped that applicants will have presented their theses during the last five years
Personal qualities
Ability to work independently as well as collaboratively in an interdisciplinary team
Motivation for applied research with real-world industrial impact
Structured and solution-oriented approach to complex problems
Personal qualities and suitability for the position will be emphasised.
We offer
Professional development in a large, exciting and socially influential organisation
A positive, inclusive and diverse working environment
Modern facilities and a comprehensive set of welfare offers
The position is remunerated according to the State Salary Scale, salary plan 17.510, code 1109 researcher (with PhD), NOK 650 000 – 750 000 gross salary per year. For code 1108 researcher (with master), NOK 540 000 – 630 000 Higher salary grades may be considered for particularly well-qualified applicants. A compulsory pension contribution to the Norwegian Public Service Pension Fund is deducted from the pay according to current statutory provisions.
General information
UiA is an open and inclusive university. We believe that diversity enriches the workplace and makes us better. We therefore encourage qualified candidates to apply for the position independent of gender, age, cultural background, disability or an incomplete CV.
The successful applicant will have rights and obligations in accordance with the current regulations for the position, and organisational changes and changes in the duties and responsibilities of the position must be expected. The engagement is to be made in accordance with the regulations in force concerning the acts relating to Control of Export of Strategic Goods, Services and Technology. Appointment is made by the University of Agder’s Appointments Committee for Teaching and Research Positions.
A background check may be conducted to verify information that appears in available documents. Background checks are always done with the applicant’s consent, and employment in the position requires an approved background check. Relevant applicants will receive further information.
Short-listed applicants will be invited for interview. With the applicant’s permission, UiA will also conduct a reference check before appointment. Read more about the employment process.
In accordance with the Freedom of Information Act § 25 (2), applicants may request that they are not identified in the open list of applicants. The University, however, reserves the right to publish the names of applicants. Applicants will be advised of the University’s intention to exercise this right.
Application
The application and any necessary information about education and experience (including diplomas and certificates) are to be sent electronically. Use the link “Apply for this job”.
The following documentation must be uploaded electronically:
Certificate with grades
Doctoral thesis (or Master’s thesis if applicable)
Up to 10 academic articles and R&D projects which the candidate wishes to particularly emphasize for the assessment process
A description of the applicant’s research interests and its relevance for the advertised position
Any other relevant documentation
The applicant is fully responsible for submitting complete digital documentation before the closing date. All documentation must be available in a Scandinavian language or English.
