Postdoctoral Fellow in machine learning with a focus on medical applications - Hiring in process/Finished, not possible to apply

The Computational Biology and Biological Physics group has an opening for a two-year postdoctoral fellow in machine learning with a focus on medical applications, starting in April, or by agreement.

Introduction
The Computational Biology & Biological Physics group at the Department of Astronomy and Theoretical Physics is recruiting a postdoctoral fellow for a project with the overall aim to develop a medical decision support tool in order to improve efficiency and increase equity in the treatment of patients with chest pain. The proposed solution is based on machine learning using electronic health records (EHR) (including electrocardiograms) and extensive health care registers. The project is interdisciplinary and includes partners from other faculties and healthcare providers.

Work duties
The successful candidate will work in the area of machine learning where labeled extracted data sets will be used to develop and validate new risk prediction models for patients with chest pain. The work will include working with heterogeneous databases, pre-processing of medical data, development of machine learning models for diagnostic and predictive purpose. The project is truly interdisciplinary, and the successful candidate will have regular contacts with expertise in other areas such as emergency medicine, IT, epidemiology, statistics and language technology.

Qualifications
Postdoctoral fellow positions are research-focused appointments intended to form early stages of a science career. The applicants should hold a doctoral degree not older than 3 years at the application deadline.

• Candidates should have a doctoral degree in computational biology, applied mathematics, computer science, or another relevant field.
• Good knowledge in spoken and written English is prerequisite.
• Successful candidates must have a solid experience in programming including using python.
• In addition, experience in machine learning for medical applications is a prerequisite.
• Experience in natural language processing, particularly text mining or information extraction is advantageous.
• Prior experience working with heterogeneous EHR data, including electrocardiograms is advantageous.
• Good knowledge in spoken and written Swedish is advantageous.

Terms of employment
This is a full-time, fixed-term employment of a maximum of 2 years. The period of employment is determined in accordance with the agreement “Avtal om tidsbegränsad anställning som postdoktor” (“Agreement on fixed-term employment as a post-doctoral fellow”) between Lund University, SACO-S, OFR/S and SEKO, dated 4 September 2008.

Application procedure
The application should consist of a CV, a copy of the doctoral degree diploma, a brief research plan and a publication list.