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Umeå University is one of Sweden’s largest higher education institutions with over 37,000 students and about 4,700 employees. The University offers a diversity of high-quality education and world-leading research in several fields. Notably, the groundbreaking discovery of the CRISPR-Cas9 gene-editing tool, which was awarded the Nobel Prize in Chemistry, was made here. At Umeå University, everything is close. Our cohesive campuses make it easy to meet, work together and exchange knowledge, which promotes a dynamic and open culture.
The ongoing societal transformation and large green investments in northern Sweden create enormous opportunities and complex challenges. For Umeå University, conducting research about – and in the middle of – a society in transition is key. We also take pride in delivering education to enable regions to expand quickly and sustainably. In fact, the future is made here.
Are you interested in learning more? Read about Umeå university as a workplace
The Faculty of Medicine, which consists of 12 departments, is responsible for biomedical research and courses in the field of nursing and health care and has an extensive research and graduate education in more than 80 subjects.
The Department of Radiation Sciences is, in terms of research, a dynamic and internationally successful environment in radiology, oncology, cognitive neuroscience, radiation physics and bio-medical engineering. The department belongs to the Faculty of Medicine at Umeå University.
The Department of Radiation Sciences welcome applications for a Phd-student position. The position is for four years full-time, starting April, 2023, or by agreement.
You who are admitted to doctoral studies will be enrolled in the faculty-wide doctoral education program at the Faculty of Medicine. The doctoral program comprises 25 credits and is offered in two study variants: 25 credits spread over 8 terms (a total of 4 years) or over 12 terms (a total of 6 years), starting each autumn and spring semester. More information about the program can be found on the faculty’s web page for doctoral studies.
Lung cancer has one of the highest incidence and mortality rates among all common cancers world- wide and this has motivated large efforts in several directions, artificial intelligence (AI) included. Although computer-aided detection and computer-aided classification tools have taken more important roles in supporting the physicians in the management of the disease, early detection of suspicious lung nodules is still an open issue; furthermore, the possibility of predicting the clinical outcomes is another raising challenge that, if properly addressed, would help tailoring personalised cures to patients.
The recent proliferation of multimodal data collected for each patient, such as CT, PET, histology whole slide images, electronic health records, genomics, proteomics, and sensor readings from medical IoT devices has paved the way to develop more advanced AI methodologies, motivated also by the promising results attained by deep learning in other fields, such as computer vision and natural language processing. However, most of the tools nowadays available consider only unimodal data so that there is an urgent demand for studying multimodal (deep) learning models for integrating multiple sources of information. Furthermore, to push forward possible applications of AI in clinics, we should encompass the lack of models’ interpretability: indeed, even when the multimodal models perform well, they cannot support doctors as black boxes without providing any feedback on the reasons for the decisions taken. Hence, within this project, we are interested in studying multimodal deep learning as well as in explaining the decisions taken by this paradigm. This project also investigates methods to make multimodal architectures robust to data-in-the-wild, where we could have missing data, missing modalities, data scarcity, and noisy data (e.g., low dose CT scans). This project also addresses the challenge of multimodal explanations, which are completely missing in healthcare. The availability of trustworthy, fair, and transparent models would help physicians, regulators, and patients to trust AI.
During the PhD project, the student will develop such techniques for application to develop better diagnostic and prognostic tools that would help fighting lung cancer and tailoring personalised treatment decisions.
Admission requirements
General admission requirements
To be admitted for doctoral studies the applicant is required to hold a University degree of at least 240 credits (ECTS), of which at least 60 credits at advanced level, or equivalent levels of education otherwise acquired in Sweden or abroad. Applicants who plan to satisfy the requirements by attaining the University or equivalent levels of education by and no later than February 28th 2023 are also eligible to apply.
Specific admission requirements
Good knowledge in English, both written and spoken, is required. The candidate is assessed for these skills during a presentation of the research plan in English for the department's doctoral education group.
The applicant's degree, or through supplementary education, should have a specialization that includes, and ideally combines, computer science and artificial intelligence. The ideal candidate has studied during the university education artificial intelligence at least at the basic level, and he/she has experience with programming languages, such as Python, Matlab, etc..
Terms of employment
The position is intended to result in a doctoral degree and the main task of the PhD student is to pursue doctoral studies, which include participation in research and postgraduate courses as well as journal clubs, seminars.
Application
The application should contain:
The application must be written in Swedish or English. The application must be completed using our e-recruitment system Varbi no later than 2023-03-12.
Salary
In accordance to PhD student local salary agreement.
Other information
For additional information about the position, please contact Prof. Paolo Soda, paolo.soda@umu.se
More about us
For more information about the Department of Radiation Sciences, please visit our website
We look forward to receiving your application!
| Type of employment | Temporary position |
|---|---|
| Contract type | Full time |
| First day of employment | enligt överenskommelse |
| Salary | Månadslön |
| Number of positions | 1 |
| Full-time equivalent | 100% |
| City | Umeå |
| County | Västerbottens län |
| Country | Sweden |
| Reference number | AN 2.2.1-271-23 |
| Union representative |
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| Published | 13.Feb.2023 |
| Last application date | 12.Mar.2023 |