PhD student in PET and machine learning - Hiring in process/Finished, not possible to apply

The Department of Surgical Sciences (IKV) is part of the Disciplinary Domain of Medicine and Pharmacy. IKV has about 160 employees and 200 registered doctoral (third cycle) students, the majority of which are clinically active doctors and nurses. The yearly turnover is SEK 152 million. Research is conducted at the department, as well as teaching in the first, second and third cycles. Teaching is, among other areas, conducted within the education programmes for doctors, nurses and specialty nurses. Research is conducted in research groups organised according to surgical specialties and closely aligned subject areas, such as anaesthesia and intensive care and radiology. A great deal of research is conducted in cooperation between separate research groups within IKV and other departments at Uppsala University, as well as other universities in Sweden and the rest of the world. Many of the department's employees have combined positions at the University Hospital.

The research group for radiology is now looking to extend their team with a PhD student in PET and machine learning!

PET (positron emission tomography) is a medical imaging technique that uses molecules labelled with positron-emitting isotopes, so-called tracers. PET is a quantitative method, that is, it not only images the distribution of tracers in the body, but can also measure absolute concentrations, which can then be used to measure biological parameters such as glucose metabolism, blood flow, receptor availability, etc. The PET centre at Uppsala University Hospital is a multidisciplinary environment, where clinical staff, chemists, physicists, biologists etc, work together to perform clinical imaging and clinical research. A major focus of the molecular imaging and medical physics research group is on the introduction of new tracers and quantitative methods in routine clinical diagnosis.

Duties
We are looking for a PhD student that will develop and implement methods for differential diagnosis of neurodegenerative diseases based on PET and machine learning. Specific aims of the project are to identify characteristic patterns of dopamine transporter availability and hypo- and hyperperfusion in parkinsonian disorders, to develop supervised machine learning-based classification for differential diagnosis of parkinsonism using PET, and to investigate the interaction between dopamine transporter availability and regional perfusion in healthy subjects and parkinsonism.

The Ph.D. student shall primarily focus on postgraduate studies, but other duties related to teaching and administrative work may be involved, up to a maximum of 20 % of the time. 

Requirements
To meet the entry requirements for doctoral studies, you must

• hold a Master’s (second-cycle) degree in information technology, machine learning, (technical) physics or equvalent, or
• have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including an independent project worth at least 15 credits, or
• have acquired substantially equivalent knowledge in some other way.

Prior experience with machine learning and (neuro)imaging or medical image analysis are essential. Programming skills in Matlab, R, or similar are required. The applicant must have a good ability to work independently as well as being able to collaborate with others. A prerequisite is competence in written and oral English along with the ability to convey your thoughts in a structured manner. 

Application
Please attach CV, copies of degree certificates and grades, master thesis, any publications and a short description of your research interests. Please also attach contact information to references and/or a recommendation letter. 

Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University's rules and guidelines.

About the employment
The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %. Starting date 2024-03-01 or as agreed. Placement: Uppsala

For further information about the position, please contact: Professor Mark Lubberink, mark.lubberink@uu.se. For questions regarding the employment process, please contact HR-representative Louise Lundqvist, louise.lundqvist@uu.se 

Please submit your application by 15th of January 2024, UFV-PA 2023/4914.

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