Doctoral researcher in AI and Machine Learning for Biomedical Imaging

About Us

The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character.

The Luxembourg Centre for Systems Biomedicine (LCSB) is an interdisciplinary research centre of the University of Luxembourg.

We conduct fundamental and translational research in the field of Systems Biology and Biomedicine – in the lab, in the clinic and in silico. We focus on neurodegenerative processes and are especially interested in Alzheimer’s and Parkinson’s disease and their contributing factors. The LCSB recruits talented scientists from various disciplines: computer scientists, mathematicians, biologists, chemists, engineers, physicists and clinicians from more than 50 countries currently work at the LCSB. We excel because we are truly interdisciplinary, and together we contribute to science and society.

Your role

The Junior Research Group in AI in Biomedical Imaging conducts applied AI research focused on biomedical image computing. Our work involves developing state-of-the-art methods for image segmentation, detection, classification, predictive modelling, and image enhancement. We aim to build more trustworthy and robust AI models that can address domain-specific challenges in biomedical applications. The PhD project will focus on translating and enhancing cutting-edge algorithms from AI research into concrete applications in biomedical imaging, with current emphasis on neurodegenerative diseases. The successful candidate will join an interdisciplinary institute working at the interface of computer science, artificial intelligence, and biomedical research.

Your profile

• Master’s degree in computer science or related discipline
• Experience with Python and recent deep learning frameworks (e.g. Pytorch, MONAI)
• Strong interest in image analysis / computer vision and pattern recognition, including but not limited to biomedical applications
• Strong interest in applied machine learning, including but not limited to deep learning
• Experience utilising GPU enabled High-Performance Computing environments is an asset
• Open minded critical thinker, willing to actively contribute to the further development of multi-cultural and multi-disciplinary research team
• Very strong organisational and analytical skills
• Enthusiasm and great commitment to research
  
  

LanguageRequiremets:

Applicants must demonstrate at least B2-level proficiency in the language of their thesis. For details and accepted certificates, please visit the Application for admission – Doctoral Candidates.

We offer

• Multilingual and international character. Modern institution with a personal atmosphere. Staff coming from 90 countries. Member of the “University of the Greater Region” (UniGR)
• A modern and dynamic university. High-quality equipment. Close ties to the business world and to the Luxembourg labour market. A unique urban site with excellent infrastructure
• A partner for society and industry. Cooperation with European institutions, innovative companies, the Financial Centre and with numerous non-academic partners such as ministries, local governments, associations, NGOs …
  
  

How to apply

Applications should include:

• Curriculum Vitae
• Cover letter presenting your motivation for this doctoral thesis topic, and explaining how your qualifications and aspirations align with its academic focus
• Transcript of all modules and results from university-level courses taken
  
  

All qualified individuals are encouraged to apply. In line with our values, the University of Luxembourg promotes an inclusive culture. We encourage applications from individuals of all backgrounds and are dedicated to upholding equality and respect for our employees and students.

General information:

• Contract Type: Fixed Term Contract 36 Month
• Work Hours: Full Time 40.0 Hours per Week
• Location: Belval Campus
• Internal Title: Doctoral Researcher
• Job Reference: UOL07836
  
  

The yearly gross salary for every PhD at the UL is EUR 41976 (full time).