Postdoctoral scholarship in computational pathology and computer
Karolinska Institutet, Department of Medical Epidemiology and Bi / Biomedicinjobb / Stockholm
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hela Sverige Visa alla jobb hos Karolinska Institutet, Department of Medical Epidemiology and Bi i Stockholm The Department of Medical Epidemiology and Biostatistics (MEB) is among the largest departments of epidemiology in Europe with special focus on increasing our knowledge of the aetiology of different diseases. Our department consists of researchers, doctoral students, biostatisticians, data collectors and database administrators as well as administrative personnel, in total some 250 staff. The department is situated at campus Solna. Further information can be found at ki.se/meb
Do you want to contribute to top quality medical research?
Division
The predictive medicine group is focused on application of machine learning and AI for development and validation of predictive models for biomedical applications, with a particular interest in cancer precision medicine and computational pathology.
Our research is based on large and high-dimensional datasets across multiple modalities, including molecular, clinical and medical imaging data.
This is a role of 1 year with possibility of extension for 1 year.
Duties
This postdoctoral project is focused on development and validation of deep learning models and image processing methodologies for cancer histopathology image data. Machine learning and deep learning techniques are central in this computational pathology project aimed at improving cancer patient stratification. As a postdoctoral scholarship holder you will be involved with implementation and evaluation of machine learning / deep learning models, and/or contribute to development of new methodologies and strategies in the domain. The project is based on unique and large in-house datasets. The role would suit applicants who are interested in developing a career in the intersection of machine learning, epidemiology and medical sciences. The Cancer Histopathology IMage Epidemiology (CHIME) project focus on large-scale studies that include histopathology image data and clinical information.
We believe that you either have a strong background in computer vision research using deep learning and an interest in medical research, or that you have a background in quantitative medical research based on large datasets (e.g. biostatistics or epidemiology) with research experience in applied image analysis and deep learning. The successful scholarship holder will join an interdisciplinary research team with strong collaboration with clinical departments and other national and international research groups.
To qualify, the applicant must hold, or expect to receive, a PhD degree (or equivalent) in a relevant subject and have a strong interest in quantitative aspects of medical research. Prior knowledge and research experience in a relevant quantitative field (e.g. machine learning, AI, epidemiology, computer vision, biostatistics, epidemiology) is required.
It is desirable that the candidate has prior experience from large-scale analysis of biomedical data and/or applied machine learning/deep learning/AI. Prior experience from cancer-related research or medical image research is desirable. The applicant should have previous knowledge and experience in programming in Python, and it is desirable that the applicant also has prior experience of working with common deep learning frameworks, such as Tensorflow/Keras or Pytorch. The applicant should be able to collaborate as part of a team as well as independently and be able to organize and prioritise his/her own tasks with minimal supervision. Excellent written and oral communication skills in English are required.
For further information please contact Associate Professor Mattias Rantalainen (email: mattias.
rantalainen@ki.se)
Entry requirements
Scholarships for postdoctoral qualification can be established for foreign researchers who place their qualifications in Sweden. The purpose of scholarships for postdoctoral qualification is to promote internationalization and contribute to research qualification after a doctorate or equivalent.
A scholarship for carrying out postdoctoral research can be granted for a maximum of two years within a four year period following the receipt of a doctoral degree or equivalent.
To be eligible for a postdoctoral scholarship, the person must have obtained a doctorate or a foreign degree deemed to be equivalent to a doctorate. Applicants who have not completed a doctorate at the end of the application period may also apply, provided that all requirements for a completed degree are met before the (intended) start date of the post doctoral education.
The head of the department determines whether their previous training and scholarly qualifications correspond to a Swedish doctorate or higher.
Location:Solna
Type of scholarship
The amount is tax free and it is set for twelve months at a time, paid out on a six months basis. In exceptional cases, shorter periods may be acceptable.
Application process
An application must contain the following documents in English or Swedish:
- A complete curriculum vitae, including date of the thesis defence, title of the thesis, previous academic positions, academic title, current position, academic distinctions, and committee work
- A complete list of publications
- A summary of current work (no more than one page)
The application is to be submitted on the Varbi recruitment system.
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Varaktighet, arbetstid
100%. Tillträde: According to agreement Visstidsanställning
Publiceringsdatum2021-03-09ErsättningMonthly
Så ansöker duSista dag att ansöka är 2021-03-30
Klicka på denna länk för att göra din ansökanFöretagKarolinska Institutet, Department of Medical Epidemiology and Bi
Arbetsgivarens referens Arbetsgivarens referens för detta jobb är "STÖD 2-1154/2021".
Omfattning Detta är ett heltidsjobb.
Arbetsgivare Karolinska Inst (org.nr 202100-2973)
Arbetsplats Karolinska Institutet, Department of Medical Epidemiology and Biostatistics
Jobbnummer 5617443
Observera att sista ansökningsdag har passerat.