The Norwegian University of Science and Technology (NTNU) invites applications for two fully funded PhD positions in Modelling Strength and Failure in Recycled Aluminium Alloys at the Department of Structural Engineering, based in Trondheim, Norway. These positions are part of the Centre for Research-based Innovation SFI FAST – Future Aluminium Structures and will be linked to the SIMLab (Structural Impact Laboratory) research group. Application Deadline: March 16, 2026 Employment Duration: 3 years for doctoral work (plus 1 year career promotion work) Proposed Start Date: August 1, 2026

About NTNU

NTNU is a leading Norwegian university with a strong technical-scientific profile and a focus on professional education. The university employs around 9,000 staff and educates 43,000 students, contributing to research and innovation for societal development. Its headquarters are located in Trondheim, Norway’s technology and research hub.

About SFI FAST – Future Aluminium Structures

SFI FAST is a national Centre for Research-based Innovation funded by the Research Council of Norway and the aluminium industry. Led by NTNU in collaboration with SINTEF and 16 industrial partners, FAST runs from 2026 to 2033 and focuses on the sustainable use of post-consumer scrap (PCS) aluminium in high-value, structural, and safety-critical products. Key goals include:

• Developing scientific knowledge and technological tools for recycled aluminium
• Creating the FAST Virtual Lab, a digital framework integrating experimental data, physics-based models, and data-driven methods
• Training over 18 PhD and postdoctoral researchers and more than 100 MSc students in advanced aluminium research

PhD Position 1: Modelling Plastic Flow and Fracture in Recycled Aluminium Alloys

This project investigates how microstructural heterogeneity in recycled aluminium alloys influences ductility, strain localization, and fracture behaviour. The candidate will combine experimental characterization and multi-scale modelling to predict material performance. Key Responsibilities:

• Characterize microstructures using SEM/EBSD, microCT, and in situ mechanical testing
• Develop microstructure-informed models of plasticity and fracture
• Investigate the effects of particle clustering and morphology on damage evolution
• Integrate experiments and modelling to create predictive tools

Supervisor: Associate Professor David Morin

PhD Position 2: Modelling Fillet Welds in Aluminium Structures

This project focuses on understanding stiffness and failure behaviour in fillet welds, including the impact of weld geometry, alloy composition, thermal history, and recycled material variability. The candidate will combine numerical modelling and experimental characterization to improve industrial simulations. Key Responsibilities:

• Develop improved stiffness representations for fillet welds in finite element models
• Investigate failure mechanisms, including initiation at start-stop regions
• Characterize weld properties for different alloys, processes, and recycled material
• Integrate mechanical testing, microstructural data, and thermo-mechanical modelling
• Propose modelling strategies suitable for industrial design workflows

Supervisor: Associate Professor Miguel Costas

Duties of Both Positions

• Conduct high-quality research and publish in international journals
• Collaborate with researchers and industry partners in SFI FAST
• Mentor MSc students and contribute to SIMLab activities
• Disseminate research findings to both scientific and broader audiences

Required Qualifications

• Master’s degree in solid mechanics, structural engineering, mechanical engineering, or related fields
• Strong academic record, equivalent to B or better on NTNU’s grading scale
• Eligibility for NTNU’s Faculty of Engineering Doctoral Program
• Fluency in English (spoken and written)
• Demonstrated motivation and ability to work independently and in teams

Preferred Qualifications

• Knowledge of plasticity theory and constitutive material modelling
• Experience with non-linear finite element methods (e.g., Abaqus)
• Coding skills in Python or Fortran
• Experimental work experience and data analysis
• Knowledge of aluminium alloys

Personal Competencies

Candidates should be:

• Highly motivated, curious, and enthusiastic
• Able to carry out goal-oriented and structured research
• Interested in interdisciplinary and collaborative work
• Strong communicators in both oral and written formats
• Passionate about combining theory with experimental and numerical methods

Salary and Employment Conditions

• Position Code: 1017 PhD Candidate
• Gross Annual Salary: NOK 550,800 (subject to qualifications and seniority)
• 2% statutory pension contribution deducted
• Full-time position with physical presence at NTNU
• Employment subject to State Employees Act and Norwegian export control regulations

What NTNU Offers

• Access to world-class research infrastructure at SFI FAST and SIMLab
• International and interdisciplinary research environment
• Career guidance and structured PhD training
• Open, inclusive, and collaborative workplace
• Membership in the Norwegian Public Service Pension Fund

Diversity and Equal Opportunity

NTNU values diversity and inclusion as drivers of innovation and impact. Applications are encouraged from all candidates, regardless of gender, cultural background, functional ability, or career interruptions. NTNU actively promotes gender balance and equality in scientific positions.

Application Requirements

Applicants must submit:

• CV and certificates
• Names and contact information of three referees
• Publications or other relevant research work

International applicants must provide documentation of the scope and quality of their education, including diplomas and transcripts, with diploma supplements if available. Joint publications must include a brief description of individual contributions. Applications will be evaluated based on academic qualifications, research potential, motivation, and personal suitability.

Living and Working in Trondheim, Norway

Trondheim is Norway’s technology capital, offering a rich cultural scene, excellent education, access to nature, and high-quality public services. The city has a strong research ecosystem, clean air, and a safe, family-friendly environment.

Contact Information

Academic Contacts:

• Associate Professor David Didier Morin – david.morin@ntnu.no
• Associate Professor Miguel Costas – miguel.costas@ntnu.no

Recruitment Contact:

• HR Consultant June Hovde – june.b.hovde@ntnu.no

These PhD positions provide a unique opportunity to contribute to cutting-edge research in recycled aluminium alloys, bridging experimental and computational mechanics with industrial applications. Apply now at: https://www.jobbnorge.no/en/available-jobs/job/296314/sfi-fast-phd-positions-in-modelling-strength-and-failure-in-recycled-aluminium-alloys

If you are planning to pursue a PhD in Europe at the intersection of artificial intelligence and healthcare, the opportunity at Linköping University is a strong choice. This fully funded PhD focuses on deep learning for medical imaging, with real-world applications in early lung cancer detection.

PhD Position Overview

• University: Linköping University

• Location: Linköping

• Department: Biomedical Engineering

• Supervisor: Anders Eklund

• Program: Data-Driven Life Science (DDLS)

• Degree Level: PhD

• Duration: 4–5 Years

• Deadline: May 14, 2026

Research Focus

This PhD is part of Sweden’s national Data-Driven Life Science (DDLS) initiative, aimed at advancing AI-driven biomedical research.

The project focuses on:

• Deep learning for medical image analysis

• Early detection of lung cancer

• Combining:

  • High-resolution CT scan data

  • Clinical variables (age, smoking status, etc.)

• Classifying lung nodules as benign or malignant

The research uses large-scale datasets (30,000+ subjects) and aims to improve diagnostic accuracy in healthcare systems.

Key Research Areas

• Computer vision for medical imaging

• Deep learning model development

• Multi-modal data fusion (imaging + clinical data)

• Large-scale biomedical data analysis

• AI applications in precision medicine and diagnostics

Scholarship Benefits

The PhD position offers:

• Starting salary of SEK 36,400/month (increases annually)

• Fully funded PhD training (no tuition fees)

• Access to advanced AI infrastructure, including:

  • High-performance computing systems

• Participation in a national research program (DDLS)

• Collaboration with:

  • Hospitals

  • AI research labs

• Opportunities for teaching (up to 20%)

Eligibility Criteria

Applicants must meet the following requirements:

• Master’s degree in:

  • Biomedical Engineering

  • Computer Science

  • Machine Learning

  • Electrical Engineering

  • Statistics or related fields

• Strong background in:

  • Deep learning

  • Computer vision

  • Mathematics

• Programming skills (especially Python)

• English language proficiency

Preferred qualifications:

• Experience in medical image analysis

• Knowledge of large-scale data processing

Responsibilities

Selected candidates will:

• Develop AI models for lung cancer detection

• Analyze large medical imaging datasets

• Collaborate with clinicians and researchers

• Publish research in scientific journals

• Participate in academic discussions and research meetings

Research Environment

Linköping University is one of Sweden’s leading institutions in AI and engineering, with strong links to national initiatives such as:

• DDLS (Data-Driven Life Science)

• WASP (AI research program)

• ELLIIT (IT and mobile communication research)

The research group collaborates closely with hospitals and international partners, providing a highly interdisciplinary environment.

Application Process

To apply, candidates must:

• Submit an online application before the deadline

• Include academic transcripts and CV

• Provide proof of qualifications and experience

Late or incomplete applications will not be considered.

Why Study in Sweden?

Sweden offers:

• High-quality research and education

• Strong focus on innovation and AI

• Excellent work-life balance

• International and inclusive academic culture

Final Thoughts

This PhD in Biomedical Engineering is ideal for candidates interested in AI, healthcare, and real-world impact. With access to large datasets, advanced computing resources, and interdisciplinary collaboration, it provides an excellent platform for building a career in medical AI research.

If you are planning to pursue a fully funded PhD in sustainable energy, the opportunity at University of Stavanger offers an excellent pathway into advanced research in geothermal systems. This PhD fellowship focuses on optimizing energy transfer in shallow geothermal wells, combining experimental work, modeling, and AI-driven optimization.

PhD Position Overview

• University: University of Stavanger

• Location: Stavanger

• Department: Energy and Petroleum Engineering

• Supervisor: Kristian Gjerstad

• Degree Level: PhD

• Start Date: August 2026

• Duration: 3 Years

• Deadline: May 30, 2026

Research Focus

This PhD project explores efficient and sustainable geothermal energy systems, aiming to improve heating and cooling technologies.

Key research objectives include:

• Optimizing energy transfer in shallow geothermal wells

• Reducing energy losses in circulation systems

• Improving cost efficiency per kWh

• Developing thermal and hydraulic models

• Applying AI-based and hybrid optimization methods

Key Research Questions

• How does well design affect heat transfer and energy losses?

• What operating conditions maximize energy output?

• How can models better predict geothermal system performance?

Research Tasks

The selected candidate will work on:

• Experimental studies on heat transfer and pressure loss

• Building and modifying flow loop systems

• Developing computational thermal and hydraulic models

• Applying AI and control algorithms for system optimization

• Validating models using real-world data

Expected Outcomes

The project aims to deliver:

• Advanced models for geothermal system design

• Improved energy efficiency and sustainability

• Practical solutions for future green energy systems

Scholarship Benefits

This fully funded PhD includes:

• Annual salary of NOK 550,800

• Pension and insurance benefits

• Access to Norway’s public healthcare system

• Paid parental leave and social benefits

• Free Norwegian language courses

• Relocation support

• Access to sports facilities and wellness services

Eligibility Criteria

Applicants must meet the following requirements:

• Master’s degree in:

  • Energy Engineering

  • Physics

  • Computer Science

  • Petroleum Engineering or related fields

• Strong academic record (minimum grade equivalent to B)

• Skills in:

  • Numerical modeling

  • Data analysis or AI

• Experience in:

  • Experimental work (preferred)

  • Software development

• Strong English proficiency

English Language Requirements

Applicants must provide one of the following:

• TOEFL (minimum 90)

• IELTS (minimum 6.5)

• Cambridge CAE/CPE

• PTE Academic (minimum 62)

Exemptions apply for candidates with prior education in English-speaking countries or EU/EEA programs.

Application Requirements

To apply, candidates must submit:

• CV and academic records

• Motivation letter

• Research proposal (mandatory)

• Certificates and diplomas

• Proof of English proficiency

• Relevant publications (if available)

Applications must be submitted through the official online portal.

Why Choose Stavanger?

Stavanger is a leading energy hub in Europe, making it an ideal location for research in sustainable technologies. The University of Stavanger is known for:

• Strong industry collaboration in energy sectors

• Focus on green transition and sustainability

• International research environment

• High quality of life and work-life balance

Final Thoughts

This PhD fellowship is ideal for candidates interested in renewable energy, AI-driven optimization, and engineering innovation. With strong financial support and access to cutting-edge research facilities, it offers a valuable opportunity to contribute to the future of sustainable energy systems.

If you are planning to pursue a PhD in Europe with strong financial support, the opportunity at Eindhoven University of Technology is one of the best options available. This fully funded PhD position in Statistical Bioinformatics offers competitive salary, advanced research training, and access to a world-class innovation ecosystem in the Netherlands.

Overview of the PhD Position

• Host Institution: Eindhoven University of Technology

• Location: Eindhoven

• Department: Mathematics and Computer Science

• Supervisor: Jeanine Duistermaat

• Degree Level: PhD

• Deadline: May 16, 2026

• Duration: 4 Years

• Employment Type: Full-time

Research Focus

This PhD project focuses on statistical data integration in biomedical research, particularly using advanced models for complex biological datasets.

Key research areas include:

• Development of latent variable models for multi-omics data

• Statistical modeling of longitudinal biological datasets

• Integration of datasets such as:

  • Proteomics

  • Metabolomics

  • Transcriptomics

• Assessing model performance and goodness-of-fit

• Creating R packages for practical implementation

The research contributes directly to improving understanding of disease mechanisms, diagnosis, and treatment strategies.

Scholarship Benefits

The PhD position offers a strong financial and professional package:

• Monthly salary between €3,059 – €3,881

• Annual bonuses:

  • 8.3% year-end bonus

  • 8% holiday allowance

• Pension scheme and paid leave benefits

• Travel, remote work, and internet allowances

• Access to:

  • High-quality research infrastructure

  • Training and career development programs

  • Sports and campus facilities

• Support for international candidates, including relocation assistance

Eligibility Criteria

To qualify for this PhD position, applicants must meet the following requirements:

• Master’s degree in:

  • Mathematics

  • Statistics

  • Or a closely related field

• Strong background in:

  • Statistical modeling

  • Data analysis

• Interest in biomedical applications and interdisciplinary research

• Good communication and academic writing skills

• English proficiency (minimum C1 level)

Additional advantages:

• Experience with biological datasets

• Programming skills (especially in R or similar tools)

Responsibilities

Selected candidates will:

• Develop new statistical methods for multi-omics data

• Apply models to real-world biomedical datasets

• Build and maintain software tools (R packages)

• Collaborate with interdisciplinary research teams

• Present research at conferences and publish findings

• Contribute to teaching (10–15% workload)

Application Process

To apply, candidates must submit:

• Motivation letter

• Updated CV (including publications and references)

• Academic transcripts

• Contact details of referees

Applications must be submitted through the official online portal. Incomplete or email submissions are not accepted.

Why Choose TU Eindhoven?

Eindhoven University of Technology is located in the Brainport Eindhoven region, one of Europe’s leading technology hubs. The university is known for:

• Strong collaboration with high-tech industries

• Cutting-edge research in AI, data science, and engineering

• A highly international academic environment

• Practical, impact-driven research approach

Final Thoughts

This PhD in Statistical Bioinformatics is ideal for candidates who want to combine advanced mathematics, data science, and biomedical research in a fully funded European program. With strong academic support and industry connections, it offers an excellent pathway for a research career in data-driven healthcare.

The University of Luxembourg is offering a fully funded PhD position in Civil Engineering with a focus on hybrid composite structures. This doctoral opportunity is part of the European HySCom project, aimed at developing next-generation structural systems using advanced materials such as UHPC and steel-concrete composites.

The position is hosted by the Faculty of Science, Technology and Medicine (FSTM) and provides a strong combination of experimental research, numerical modelling, and industry collaboration.

PhD Position Overview

• Level of Study: PhD (Doctoral Researcher)

• Institution: University of Luxembourg

• Faculty: Faculty of Science, Technology and Medicine (FSTM)

• Department: Engineering (DoE)

• Supervisor: Markus Schäfer

• Location: Luxembourg City (Kirchberg Campus)

• Duration: 36 months

• Start Date: June 15, 2026

• Salary: €41,976 per year (gross, full-time)

• Language Requirement: English (minimum B2 level)

Project Overview – HySCom

This PhD position is part of the Eurostars HySCom project, focusing on the development of innovative hybrid composite columns combining:

• Reinforced concrete

• Structural steel

• Steel Fibre Reinforced Ultra-High Performance Concrete (SFRUHPC)

The project is conducted in collaboration with a leading German industrial partner, providing real-world engineering applications alongside academic research.

Research Areas

The doctoral research will include:

• Material characterization of concrete and UHPC

• Development and optimization of UHPC mixtures

• Experimental testing (small-scale and large-scale structural tests)

• Push tests and column tests

• Advanced nonlinear finite element modelling (Abaqus)

• Fire and ambient condition simulations

• Structural design model development

• Parametric studies and model validation

Key Responsibilities

The selected candidate will:

• Conduct laboratory-based experimental research

• Develop and validate computational models

• Perform 3D nonlinear FEM simulations

• Publish results in international journals

• Present findings at global conferences

• Support teaching and academic activities

Eligibility Criteria

Applicants must meet the following requirements:

Education

• Master’s degree in:

  • Civil Engineering

  • Structural Engineering

  • Related engineering discipline

Technical Skills

• Strong knowledge of:

  • Structural engineering

  • Steel–concrete composite systems

  • Construction materials (concrete, UHPC)

• Familiarity with:

  • Eurocodes (EN 1990, EN 1992, EN 1993, EN 1994)

• Experience with:

  • Nonlinear FEM (preferably Abaqus, GMNIA)

Additional Requirements

• Interest in experimental and numerical research

• Strong analytical and problem-solving skills

• English proficiency (minimum B2 level)

Salary and Benefits

This is a fully funded doctoral position offering:

• €41,976 annual gross salary

• Full-time employment (40 hours/week)

• Access to advanced laboratory and simulation facilities

• Collaboration with industry partners

• Opportunities to attend international conferences

• Multicultural research environment

Required Application Documents

Applicants must submit:

• Curriculum Vitae (CV)

• Motivation letter

• Academic transcripts

• Contact details of 1–2 referees

Applications must be submitted through the official online system. Email applications are not accepted.

How to Apply

To apply for this PhD position:

1. Prepare all required documents

2. Submit your application through the official University of Luxembourg HR portal

3. Apply as early as possible (rolling selection process)

Contract Details

• Position Title: Doctoral Researcher

• Contract Type: Fixed-term (36 months)

• Working Hours: Full-time (40 hours/week)

• Campus: Kirchberg, Luxembourg

About the Faculty (FSTM)

The Faculty of Science, Technology and Medicine at the University of Luxembourg is a multidisciplinary hub covering:

• Engineering

• Computer Science

• Physics and Mathematics

• Life Sciences and Medicine

The Department of Engineering focuses on innovative and sustainable solutions for modern infrastructure and industry challenges.

Why Apply for This PhD?

This opportunity is ideal for candidates who want to:

• Work on cutting-edge composite materials and structures

• Gain hands-on laboratory and simulation experience

• Collaborate with industry partners

• Build an international research career in Europe

• Contribute to sustainable infrastructure development