Doctoral student in Applied Mathematics with specialization in Medical Image Analysis

Description of the workplace

The position will be based at the Division of Computer Vision and Machine Learning (CVML) at the Centre for Mathematical Sciences (https://www.maths.lu.se). CVML has about ten senior researchers and about 20 graduate students. Research in this area started in the mid-1980s and currently includes (i) Geometry and Computer Vision, (ii) Medical Image Analysis, and (iii) Machine Learning and Artificial Intelligence. The division has extensive experience in basic research in computer vision, image analysis and machine learning, as well as in a wide range of applications of these fields. In Medical Image Analysis, the division has worked on a wide range of areas, including radiology, magnetic resonance imaging, digital pathology, scintigraphy and ultrasound for the diagnosis of, for example, prostate cancer, breast cancer, dementia and skin cancer, as well as in cardiology and myocardial ischemia.

The Centre for Mathematical Sciences works systematically and continuously with the work environment, both physical and psycho-social. Work environment is a standing item at board meetings and is also addressed at each performance review.

This PhD position is funded by the Palmary Foundation, https://www.palmary.foundation/doktorandenstelle.

Subject description

The PhD subject is Applied Mathematics and the research focus is the development and analysis of methods for Medical Image Analysis, especially using machine learning techniques. 

In applied mathematics, mathematical methods and models are developed and analyzed, which are applicable to problems from various sciences such as physics, computer science, medicine, biology, economics and the like. Research in applied mathematics involves the development, analysis and implementation of methods and models designed to solve mathematically formulated problems. It may also include the use of such methods to develop new technologies in other sciences such as engineering, science and medicine.

Work duties

The main duties of doctoral students are to devote themselves to their research studies which includes participating in research projects and third cycle courses. The work duties will also also include teaching and other departmental duties (no more than 20%).

Detailed description of the work duties, such as:

• Participation in post-graduate courses
• Work on the research project with an increasing degree of independent research
• Teaching and other departmental work (max 20% of working hours)

The research focus of this call is the development and analysis of machine learning methods, mainly deep neural networks, for the analysis of images, image sequences or metadata, such as text, for medical applications. Examples of different questions you can work on are prostate cancer detection from microscopy images of diopsies, breast cancer detection from ultrasound, analysis of dementia progression from brain MRI images, and automatic selection of examination type from radiology referrals.

The thesis work will include development of new methods, theoretical analysis, algorithm design, planning and execution of experiments, collection of data, writing of scientific papers and presentation of the results at international conferences and in international journals.

Admission requirements 

A person meets the general admission requirements for third-cycle courses and study programmes if the applicant: 

• has been awarded a second-cycle qualification, or
• has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle, or
• has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

A person meets the specific admission requirements for third cycle studies in Applied Mathematics if the applicant has: 

• at least 90 credits relevant to the subject area, of which at least 60 credits must be at second-cycle level in the subject area and include a thesis of at least 15 credits, or
• an advanced level degree in a relevant subject area.

Additional requirements:

• Very good oral and written proficiency in English.
• Good knowledge within image analysis and machine learning.
• Experience in programming, for example in Matlab, Python or C++.

Assessment criteria

Selection for third-cycle studies is based on the student’s potential to profit from such studies. The assessment of potential is made primarily on the basis of academic results from the first and second cycle. Special attention is paid to the following: 

1. Knowledge and skills relevant to the thesis project and the subject of study.
2. An assessment of ability to work independently and to formulate and tackle research problems.
3. Written and oral communication skills.
4. Other experience relevant to the third-cycle studies, e.g. professional experience.

Other assessment criteria:

• Experience in teaching at basic level.
• A project-relevant degree project
• Experience in collaboration outside the subject area.
• Experience with any machine learning development environment, such as TensorFlow, Keras or PyTorch.. 

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant, through experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme.

We offer

Lund University is a public authority which means that employees get particular benefits, generous annual leave and an advantageous occupational pension scheme. Read more on the University website about being a Lund University employee Work at Lund University. 

Terms of employment

 Only those admitted to third cycle studies may be appointed to a doctoral studentship. Third cycle studies at LTH consist of full-time studies for 4 years. A doctoral studentship is a fixed-term employment of a maximum of 5 years (including 20% departmental duties). Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

How to apply

Applications shall be written in English and include a cover letter stating the reasons why you are interested in the position and in what way the research project corresponds to your interests and educational background. The application must also contain a CV, degree certificate or equivalent, and other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.).

Welcome to apply!