PhD Stipend in Development of an Artificial Intelligence-Based Methodology for Hemoglobinopathy Screening

Hemoglobinopathies, or structural variations in hemoglobin, and thalassemia syndromes are the most common genetic illnesses in the world. A variety of number discrimination formulae(index) based on red blood cell(RBC) parameters have been developed throughout the years to aid in the early identification of individuals. However, because of the existence of several variances and variations in RBC measurements for individuals from various geographical locations and age groups, those are unlikely to be adequate to meet the need for early screening sensitivity and specificity. The project will have a substantial influence on the early-stage categorization of individuals in order to prevent disease transmission and death.

?To improve the early screening sensitivity based on RBC parameters, the student shall evaluate data from different sources and determine the optimum circulating biomarkers for early hemoglobinopathies detection, guiding precision treatment.? The resulting algorithms are expected to predict which individuals have thalassemia syndrome with greater sensitivity and specificity by identifying optimal combinations of RBC parameters features. The potential clinical significance is that a more comprehensive blood-based workflow could aid with stratification and targeting therapy at an early stage. The new algorithm is anticipated to expedite the AI application in other clinical screening problems.

The Ph.D. project focuses on interdisciplinary research at the intersection of Statistics, Machine Learning, and Medical Data. Statistical analysis and interpretation of medical data, exploring different learning methods, comparing different machine learning models in clinical research, model sensitivity analysis, and clinical validation of the model are example research goals of this Ph.D. stipend

About the Ph.D. study:

• Develop a new algorithm for a rule-based multi-class classification problem
• Analysis and management/cleaning of medical data
• Statistical analysis for data validation and verification
• Providing statistical input for hypothesis testing
• Lead the preparation of scientific publications towards ISI journals and highly ranked international conferences

The Ph.D. candidate is expected to have:

• Bachelor's and Master's degree or a similar in Statistics, Computer Science, Computer Engineering, Mathematics, Applied Mathematics, or equivalent.
• Hands-on experience with Python, R, or other programming languages.
• Experience in creating and validating analysis datasets.
• High level of English written and spoken skills.
• Having worked with medical data before as an advantage.
• Knowledge of machine learning and deep learning algorithms in the clinical domain as an advantage.
• Strong communicative and collaborative skills in an interdisciplinary environment

You may obtain further information from Prof. Tarec Christoffer El-Galaly, Department of Hematology, Aalborg University Hospital, email:tceg@rn.dk ; Assoc. Prof. Peter Nielsen, Department of Materials and Production, Aalborg University, email:peter@mp.aau.dk , Assoc. Prof. Sören Möller, Odense University Hospital and University of Southern Denmark, email:Soren.Moller@rsyd.dk , concerning the scientific aspects of the stipend.

PhD stipends are allocated to individuals who hold a Master's degree. PhD stipends are normally for a period of 3 years. It is a prerequisite for allocation of the stipend that the candidate will be enrolled as a PhD student at the Doctoral School of Engineering and Science in accordance with the regulations of Ministerial Order No. 1039 of August 27, 2013 on the PhD Programme at the Universities and Certain Higher Artistic Educational Institutions. According to the Ministerial Order, the progress of the PhD student shall be assessed at regular points in time.

Shortlisting will be applied. This means that subsequent to the deadline for applications the head of department supported by the chair of the assessment committee will select candidates for assessment. All applicants will be informed whether they will be assessed or not.

For further information about stipends and salary as well as practical issues concerning the application procedure contact Ms. Katrine Søndergaard, The Doctoral School at The Faculty of Engineering and Science, email:kes@adm.aau.dk . 

For more information of The Doctoral School of Engineering and Science:www.phd.engineering.aau.dk  

The application is only to be submitted online by using the"Apply online" button below.

AAU wishes to reflect the diversity of society and welcomes applications from all qualified candidates regardless of personal background or belief.

Appointment and salary as a PhD fellow are according to the Ministry of Finance Circular of 15 December 2021 on the Collective Agreement for Academics in Denmark, Appendix 5, regarding PhD fellows, and with the current Circular of 11 December 2019 on the employment structure at Danish universities.