项目介绍

2024年12月21日

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

We offer a highly interdisciplinary and vibrant research environment at the international forefront of developing and implementing precision medicine strategies for improving diagnostics and treatment of cancer. The doctoral position is hosted at the Department of Medical Epidemiology and Biostatistics (MEB), located at the Solna campus of Karolinska Institutet in Northern Stockholm. The group is affiliated with the national Data Driven Life Sciences (DDLS) program and SciLifeLab (https://www.scilifelab.se/researchers/kimmo-kartasalo/), providing the student with networking and career development opportunities as a member of the DDLS Research School.

We work closely together with other groups at MEB within and beyond the department’s prostate cancer research environment, as well as with academic and industry collaborators on national and international levels. This provides ample opportunities for establishing cross-disciplinary connections and collecting international experience during the doctoral education, for example in the form of attending conferences or conducting research visits to other universities. We are also actively involved in commercialization efforts supported by the KI Innovations office to translate research findings into clinically applicable tools for improving healthcare.

The core competences of the group include large-scale image processing and analysis, development of deep learning based artificial intelligence (AI) algorithms, and efficient utilization of supercomputing systems. Our backgrounds range from computer science and mathematics to biotechnology and medicine, and we embrace a highly collegial and flexible working culture.

The doctoral student project and the duties of the doctoral student

AI and machine learning (ML) provide new opportunities for precision medicine, where data-driven approaches are applied for improved diagnostics, prognostication and treatment decisions. One of the medical disciplines that are becoming increasingly data-driven is pathology, where digital scanning of tissue samples is becoming a routine practice and provides vast amounts of image data usable as the basis for more efficient and accurate clinical management of diseases like cancer.

We apply the latest AI techniques to the analysis of digital pathology data with the aim of improving the efficiency, accuracy and reproducibility of pathological assessments. We also increasingly work on multi-modal analytics, where image data is processed together with molecular information and clinical variables to build AI models capable of estimating the most likely future course of an individual’s disease and predicting optimal therapeutic options.

The doctoral student will work in projects analyzing the performance of both in-house developed state-of-the-art AI models and publicly available foundation models in prostate cancer diagnosis, grading and prognostication across diverse patient populations and digital pathology platforms. The student will develop algorithms for new emerging solutions enabled by AI for streamlining the clinical histopathology workflow, for example by using generative AI for virtual staining and molecular profiling of tissue samples based only on routine tissue specimens, with the aim of making precision medicine approaches in pathology more accurate, scalable and cost-effective.

The doctoral student is expected to build an in-depth understanding of state-of-the-art AI and image analysis methodology and to absorb a necessary level of medical and histopathological knowledge while working as a part of an interdisciplinary team of scientists and clinicians. The position of a doctoral student involves taking an active role in driving your own research projects, with guidance from the supervisor team, and in preparing scientific research articles for publication.

What do we offer?

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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 requirementYou 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 is expected to possess the following skills and experience:

– A Master’s degree or equivalent qualifications (see “Eligibility requirements”) in a field relevant for computational pathology, for example bioinformatics, biomedical engineering, biotechnology, computer science, image/signal processing, medicine, software engineering, statistics or related fields.

– A solid understanding of the basics of ML/AI (e.g. based on a suitable educational background or prior professional experience) and an interest in building a deep understanding of the theory and practice of ML/AI.

– Good programming skills in at least one programming language, preferably demonstrable in the form of sample code or other output from earlier projects/studies (e.g. link to Github/Gitlab profile).

– Fluency in written and spoken English.

The following skills and experience are considered meriting:

– Experience of working with digital pathology data and familiarity with whole slide image file formats and tools (e.g. OpenSlide, Bioformats, QuPath, Cytomine).

– Experience of working with other (biomedical) image data from e.g. microscopy or medical imaging systems.

– Knowledge of PyTorch and/or other AI and image analysis frameworks (e.g. TensorFlow, OpenCV, MATLAB).

– Strong programming skills in Python.

– Software engineering skills, including e.g. version control, dependency management and software testing.

– Experience of high-performance computing (e.g. using cloud, compute clusters or supercomputers).

– Expertise in data management, databases and registries.

– Knowledge of and interest in pathology, oncology or other relevant medical fields.

– Demonstrated scientific writing skills (e.g. in the form of theses or publications).

– Experience of working in an interdisciplinary setting and communicating with professionals from different backgrounds.

We attach great importance to personal qualities in this recruitment. The applicant should possess excellent analytical problem-solving skills, be able to show initiative and take responsibility of their research projects, and face obstacles in their work with perseverance. The applicant must also be highly collegial and be able to work well as a member of a cross-disciplinary and diverse team.

Terms and conditions

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