PhD student position in digital pathology - Hiring in process/Finished, not possible to apply

Do you want to contribute to improving human health?

To be a doctoral student means to devote oneself to a research project under supervision of experienced researchers and following an individual study plan. For a doctoral degree, the equivalent of four years of full-time doctoral education is required.

The Department of Medical Epidemiology and Biostatistics conducts research in epidemiology and biostatistics across a broad range of areas within biomedical science. The department is among the largest of its type in Europe and has especially strong research profiles in psychiatric, cancer, reproductive, pediatric, pharmaco, genetic, and geriatric epidemiology, eating disorders, precision medicine, and biostatistics.

Part of the success of our department is due to our collaborative spirit where one factor is that researchers at the Department share and co-finance common resources (e.g., IT and an applied biostatistics group). The department is situated at campus Solna. Further information can be found at http://ki.se/en/meb

The research group

With 1.1 million new diagnoses every year, prostate cancer is the most common cancer in men in developed countries. The prostate cancer group at MEB is a highly interdisciplinary and vibrant research group at the international forefront of developing and implementing precision medicine strategies for improving diagnostics and treatment of prostate cancer. The research spans world-leading data sources, innovative clinical trials, cutting edge genomics, machine learning and artificial intelligence.

The doctoral student project and the duties of the doctoral student

The work aims at improving prostate cancer care by developing artificial intelligence techniques for digital pathology data. The biopsy Gleason grading system is the most important prognostic marker for prostate cancer but suffers from significant inter-observer variability, limiting its usefulness for individual patients. The Gleason grade is determined by pathologists based on the microscopical growth patterns of the tumor visible in tissue specimens and forms the basis for treatment decisions.

Automated deep learning systems have shown promise in accurately grading prostate cancer. We recently published results in the leading medical journal the Lancet Oncology showing that systems based on convolutional neural networks can achieve expert pathologist-level performance. We are now looking for a doctoral student with an interest in image analysis, digital pathology and deep learning to continue this work with respect to both technical methods development and towards prospective validations in clinical trials.

The candidate is expected to absorb necessary knowledge on cancer etiology and to maintain a high level of statistical and machine learning competence with focus on image analysis.

What do we offer?

Choose to work at KI-Ten reasons why
Career support for doctoral students
International staff

A creative and inspiring environment full of expertise and curiosity. Karolinska Institutet is one of the world's leading medical universities. Our vision is to pursue the development of knowledge about life and to promote a better health for all. At Karolinska Institutet, we conduct successful medical research and hold the largest range of medical education in Sweden. As a doctoral student you are offered an individual research project, a well-educated supervisor, a vast range of elective courses and the opportunity to work in a leading research group. Karolinska Institutet collaborates with prominent universities from all around the world, which ensures opportunities for international exchanges. You will be employed on a doctoral studentship which means that you receive a contractual salary. Employees also have access to our modern gym for free and receive reimbursements for medical care.

Eligibility requirements for doctoral education

In order to participate in the selection for a doctoral position, you must meet the following general (A) and specific (B) eligibility requirements at latest by the application deadline.

It is your responsibility to certify eligibility by following the instructions on the web page Entry requirements (eligibility) for doctoral education.

A) General eligibility requirement

You meet the general eligibility requirement for doctoral/third-cycle/PhD education if you:

1. have been awarded a second-cycle/advanced/master qualification (i.e. master degree), or
2. have satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the advanced/second-cycle/master level, or
3. have acquired substantially equivalent knowledge in some other way in Sweden or abroad.*

Follow the instructions on the web page Entry requirements (eligibility) for doctoral education.

*If you claim equivalent knowledge, follow the instructions on the web page Assessing equivalent knowledge for general eligibility for doctoral education.

B) Specific eligibility requirement

You meet the specific eligibility requirement for doctoral/third-cycle/PhD education if you:

- Show proficiency in English equivalent to the course English B/English 6 at Swedish upper secondary school.

Follow the instructions on the web page English language requirements for doctoral education.

Verification of your documents Karolinska Institutet checks the authenticity of your documents. Karolinska Institutet reserves the right to revoke admission if supporting documents are discovered to be fraudulent. Submission of false documents is a violation of Swedish law and is considered grounds for legal action.

(A) and (B) can only be certified by the documentation requirement for doctoral education.

Skills and personal qualities

The applicant should possess a Master’s degree in Computer Science and Engineering, or a closely related field such as signal processing, machine learning, statistics, or bioinformatics.

Expertise in image analysis and deep learning models, with a focus on object detection and segmentation models are considered merits, as well as experience from digital pathology. Experience in research related to cell graphs and Graph Neural Networks (GNNs) is also a merit [CWB1] in particular within digital pathology environments.

Proven ability in cross-disciplinary collaboration, particularly with computer science and medical researchers.

Strong programming skills are required, with proficiency in Python, and familiarity with machine learning and image analysis frameworks (e.g., TensorFlow, PyTorch, Keras, Scikit-learn, OpenCV) is a must. Experience with whole slide image file formats and tools (e.g., OpenSlide, Bioformats, QuPath, Cytomine), as well as databases and high-performance cluster/cloud computing are considered merits.[CWB2] 

Fluency in English is mandatory. Previous experience in writing and publishing research manuscripts, particularly in the context of digital pathology and machine learning applications in medicine, is a plus.

Personal qualities
The applicant should be highly collegial and possess the ability to work effectively in a cross-disciplinary team, reflecting a history of successful collaboration with computer science and medical researchers. The ability to work independently, coupled with exceptional organizational skills and a proven track record of developing structured solutions to complex problems, is crucial.

The candidate should have excellent analytical skills, demonstrate curiosity and creativity, and have the capacity to take responsibility for research projects, as shown by previous supervision of students and involvement in research at the intersection of computer science and pathology.

Terms and conditions

The doctoral student will be employed on a doctoral studentship maximum 4 years full-time.

Application process

Submit your application and supporting documents through the Varbi recruitment system. Use the button in the top right corner and follow the instructions. We prefer that your application is written in English, but you can also apply in Swedish.

Your application must contain the following documents:

- A personal letter and a curriculum vitae
- Degree projects and previous publications, if any
- Any other documentation showing the desirable skills and personal qualities described above
- Documents certifying your general eligibility (see A above)
- Documents certifying your specific eligibility (see B above)

Selection

A selection will be made among eligible applicants on the basis of the ability to benefit from doctoral education. The qualifications of the applicants will be evaluated on an overall basis.

Karolinska Institutet uses the following bases of assessment:

- Documented subject knowledge of relevance to the area of research
- Analytical skill
- Other documented knowledge or experience that may be relevant to doctoral studies in the subject.

All applicants will be informed when the recruitment is completed.

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