Apply for this job
Apply no later than 5 May 2017
Apply for the job at DTU Electrical Eng by completing the following form.
The Department of Electrical Engineering, Biomedical Engineering group, invites applicants for a 3-year PhD study in advanced biomedical signal processing and machine learning of multimodal multichannel signals measured on patients after cancer surgery.
Major elective cancer surgery in the abdomen is associated with substantial morbidity and mortality risk despite optimized anesthesia and surgical techniques. Thus, it is estimated that severe morbidity occur in 25-35% after abdominal cancer surgery within the first 30 days. Acute mortality is similarly unacceptable high, evidenced by national data showing 8- 9% in-hospital mortality after upper abdominal surgery, and around 4-6% within the first 90 postoperative days. The high morbidity and mortality after abdominal cancer surgery is in discrepancy with the fact that these procedures are performed with a curative or life- prolonging aim.
The observed high complication rate after abdominal cancer surgery may be due to late detection of severe complications and potentially to late treatment when the condition has progressed past the point of no return. Thus, all Danish hospitals currently use the Early Warning Score (EWS) system, where a number of physiological parameters are recorded once every 12 hours.
However, no proven survival benefit of this approach has been shown, and a number of critical events (e.g. desaturation) can occur between the fixed 12 hours’ measurements without being detected. Thus, it has been shown that the 12 hours’ routine measurements (EWS) only detects 5% of the serious cases of severe hypoxia in the wards, when compared to continuous measurements.
Theoretically, a continuous (24/7) system that could detect patterns prior to serious postsurgical events anytime would be superior to the current EWS, as preventive interventions can be initiated before the disease becomes manifest, and not until several hours after the damage is done.
Using biomedical signal analysis, processing, and interpretation of a wide range of continuously measured physiological parameters, we will seek to develop an algorithm for prediction of severe postoperative complications after abdominal cancer surgery and define a set of criteria for intervention against severe postoperative events. Hence, the aim is to achieve continuous patient surveillance with a ’wear and forget’ device, for early intervention to prevent the development of a number of postoperative complications, with reduced morbidity, mortality and health care costs, increasing overall survival after cancer treatment. The PhD scholarship is partly funded by Danish Cancer Society “Knæk Cancer” and DTU Elektro.
The purpose of the PhD project is to design and implement a full-automatic clinical support system capable of detection and prediction of severe postoperative complications after abdominal cancer surgery.
The PhD student will work in a highly collaborative environment, carry out data analysis and integration, and apply best-in-class algorithms – and develop new algorithms – that directly address the motivating biological questions. Collaboration across other labs and across departments is encouraged.
Experience in advanced biomedical signal processing, programming, advanced mathematics, and a solid background in statistical analysis are needed. Dedicated and excellent interpersonal skills and the ability to interact effectively with members of the research teams are essential to the success of the individual in this position. The successful candidate must be able to learn and work independently, yet collaborate effectively with co-workers.
Approval and Enrolment
- MSc in biomedical/electrical engineering, biomedical data science or equivalent qualification with preferably publication record
- Strong knowledge and experience of advanced biomedical signal processing methods and algorithms
- Excellent command of English (written and spoken) as well as technical writing.
- An understanding of advanced mathematical & statistical principles behind current best practices in high-throughput data analysis.
- Strong experience in the use of a high-level programming language such as MATLAB, C, C++, r for complex signal/data analysis.
- Preferably familiarity with high performance computing and computing clusters.
- Ability and willingness to mentor junior students.
- Ability to provide advice to lab members on appropriate data analysis approaches.
- Ability to work both independently and collaboratively in complex organizations (technical/medical), and to handle several concurrent projects.
- Exceptionally strong communication and interpersonal skills.
- Excellent data presentation and visualization skills.
- Ability to effectively present complex results in a clear and concise manner that is accessible to a diverse audience.
- Enthusiasm for learning more.
The scholarships for the PhD degree are subject to academic approval, and the candidates will be enrolled in one of the general degree programmes of DTU. For information about the general requirements for enrolment and the general planning of the scholarship studies, please see the DTU PhD Guide
The assessment of the applicants will be made by Associate Professor Helge B.D. Sørensen (DTU), Associate Professor Eske Aasvang (Rigshospitalet CPH University), and MD PhD Christian Meyhoff (Bispebjerg University Hospital).We offer
We offer an interesting and challenging job in an international environment focusing on education, research, scientific advice and innovation, which contribute to enhancing the economy and improving social welfare. We strive for academic excellence, collegial respect and freedom tempered by responsibility. The Technical University of Denmark (DTU) is a leading technical university in northern Europe and benchmarks with the best universities in the world. Rigshospitalet and Bispebjerg University Hospital are leading university hospitals in Denmark. Salary and appointment terms
The salary and appointment terms are consistent with the current rules for PhD degree students. The period of employment is 3 years.Workplace
- DTU Lyngby campus
- Copenhagen University Hospital/Rigshospitalet, Copenhagen
- Bispebjerg University Hospital
Further information may be obtained from Associate Professor, PhD Helge B.D. Sørensen, firstname.lastname@example.org,
Biomedical Engineering group, Department of Electrical Engineering, Technical University of Denmark (DTU).
You can read more about the Department of Electrical Engineering on www.elektro.dtu.dk
Please submit your online application no later than 5 May 2017
. Applications must be submitted as one pdf file
containing all materials to be given consideration. To apply, please open the link "Apply online," fill in the online application form, and attach all your materials in English in one pdf file
. The file must include:
- A letter motivating the application (cover letter)
- Curriculum vitae
- Grade transcripts and BSc/MSc diploma
- Excel sheet with translation of grades to the Danish grading system (see guidelines and excel spreadsheet here)
Candidates may apply prior to obtaining their master's degree, but cannot begin before having received it.
All qualified candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.
The Department of Electrical Engineering is the central department at the Technical University of Denmark within electrical and biomedical engineering. Including PhD students we have a total of 260 staff members. It is our goal to ensure research and engineering training at the highest international level. The department is organized into 8 sections and a number of cross professional centers. You can read more about Department of Electrical Engineering and the Biomedical Engineering Group on www.elektro.dtu.dk.
DTU is a technical university providing internationally leading research, education, innovation and public service. Our staff of 5,800 advance science and technology to create innovative solutions that meet the demands of society; and our 10,600 students are being educated to address the technological challenges of the future. DTU is an independent academic university collaborating globally with business, industry, government, and public agencies.