Doctoral (PhD) student position in Biostatistics/Machine Learning - Hiring in process/Finished, not possible to apply

Do you want to contribute to top quality medical research?

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 research group

A doctoral student position is available within the biostatistics group at the Department of Medical Epidemiology and Biostatistics (MEB) at Karolinska Institutet (https://ki.se/meb), Stockholm. The research project is part of a cross-university collaboration within a national e-science program and will be carried out in collaboration with researchers both at MEB and at the Division of Robotics, Perception and Learning (RPL) at KTH Royal Institute of Technology (https://www.kth.se/rpl), Stockholm. The biostatistics group at MEB, KI comprises 5 professors, 3 senior lecturers, 9 PhD-level statisticians, 4 masters-level statisticians, and 10 doctoral students. The group is involved in a wide variety of research projects, including population-based cohort and case-control studies, twin and family studies, survival analyses, predictive modeling, genetic epidemiology and bioinformatics. The collaborating group at RPL, KTH comprises one professor, 6 doctoral students, two research engineers, and a varying number of master students. The group is targeted towards probabilistic machine learning and probabilistic deep learning, with a wide range of collaborations in medicine, veterinary science, autonomous vehicles, performing arts, and e-science.

The doctoral student project and the duties of the doctoral student

The aim of this doctoral project is to develop algorithms that can efficiently fit complex, latent variable models to large-scale Swedish health and population register data, such as data from the Swedish Multi-generation Register or the Stockholm PSA and Biopsy Register. The models include grouped survival models and joint models for time-to-event and longitudinal data.

There are a number of approaches for efficient approximate likelihood and Bayesian computations which have been developed and widely applied in machine learning algorithms that are not widely known in statistics. The idea of this project is to explore the use of some of these approaches in fitting more traditional, yet complex, biostatistical regression models to large-scale epidemiological data, where exact computations of likelihoods will be infeasible and Monte Carlo sampling techniques reach their limits. The doctoral project will be a mix of statistical theory, computational statistics and applications. The computational languages will primarily be R and C++.

The successful applicant’s time will be divided between MEB, KI, under the supervision of Mark Clements (Senior Lecturer in Biostatistics), Benjamin Christoffersen (Postdoctoral Researcher in Biostatistics) and Keith Humphreys (Professor in Biostatistics), and RPL, KTH, under the supervision of Hedvig Kjellström (Professor in Computer Science). You will have access to high-end GPU and CPU computational resources for your research.

What do we offer?

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 position requires a second-cycle university degree in statistics/mathematics/computer science or a closely related field. Experience in mixed effects modelling, computational statistics or survival analysis would be ideal. The exact topic of the position will depend on the successful applicant's background and interests.

Personal qualities for the role include being independent, structured, performance-oriented and with strong analytical abilities.

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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