Do you want to contribute to top quality medical research?
Department of Medicine, Huddinge
se/en/medh/department-of-medicine-huddinge conducts preclinical and clinical research in a variety of areas and undergraduate and graduate education. The department is internationally oriented and consists of seven units with a total of nearly 60 research groups, including three research centres.
Focusing on cardiovascular diseases, the goal of the research of Dr. Björkegren is to use multi-modal big data analysis to create reliable network models of human biology and disease. Network models have enormous potential to improve our ability to predict disease risk, identify new therapeutic targets, and to monitor molecular effects of treatments. To achieve this goal, we have designed and generated a range of clinical datasets of cardiovascular disease that combine detailed clinical characteristics with imaging, genomics, proteomics, and other types of data. Dr. Björkegrens research has long focused on systems analyses to generate network models from large genomic datasets—both from CAD patients in the clinic and from cellular and mouse models of atherosclerosis progression and regression in the laboratory. Throughout the last decade, we have designed and led a range of clinical and mouse model studies to elucidate the inherit complexity of CAD. As one of the first clinical scientists to apply the emerging technologies of molecular profiling to large patient cohorts, we have revealed the role of functionally associated genes in several molecular networks that drive CAD. A common complex disease such as CAD cannot be understood nor cured by targeting isolated genes. Rather, the focus needs to be on molecular disease processes mirrored by regulatory-gene networks that capture the combined effects of many genetic and environmental risk factors. To this end, much of Dr. Björkegrens time has gone into gathering a truly unique biobank from CAD patients undergoing different forms of heart surgery. The Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) is a joint study initiative between the cardiovascular chief surgeon at the Tartu University Hospital in Estonia, Dr. Arno Ruusalepp.. Using the STARNET bio-ban, we have generated RNA sequence data from up to nine CAD-relevant tissues isolated from over 500 hundred clinically well–characterized patients. This unprecedented dataset is the main resource for our current efforts to generate network models that predict the risk for and clinical outcomes of CAD. In addition to Karolinska, Dr Björkegren runs a lab. at the Mount Sinai Hospital, NY with world-leaading bioinforamtik competences.
We are looking for one ambitious bioinformatician with solid disease-focused genome-wide
bioinformatics/computational biology skills to join our highly accomplished team. The position as bioinformatician aim towards proteo-transcriptomics studies to execute novel bioinformatics and statistical methods for transcriptomics, proteomics, and multiparametric proteotranscriptomics analysis to identify biomarkers, map intercellular pathways of CAD and their relation to cardiometabolic disorders as well as the importance of the gut microbiota. The candidate should thus have a strong background in bioinformatics.
Mastery of the core concepts of Bioinformatics is expected. These include (a) advanced methods in computational biology towards the machine learning in medical science, (b) knowledge and awareness of the basic principles and concepts of biology, computer science and mathematics, (c) the design and implementation of relational databases, and (e) the construction of predictive mathematical models of biological systems. The research assistant is expected to analysis large amount of omics data mainly transcriptomics and proteomics.
The candidate must hold a at least Masters Degree.
We seek a highly motivated candidate with a demonstrated record of scientific excellence.
We favor candidates who:
• Have second-cycle background in genetics and/or bioinformatics.
• Interest and prior experience in transcriptomics and proteomics analysis are essential. Provide github account details.
• The applicant should have strong competence in Python, R-programming language and network modelling.
• Experience of analysis of CVD and related diseases as well as microbiota
• The applicant should be able to work well in a multidisciplinary team, as well as having the ability to work independently.
• The applicant should possess good organizational skills and know how to multitask.
• Excellent communication skills in both spoken and written English is of utmost importance.
We welcome a creative, hardworking and enthusiastic team-player to our group. The candidate is expected to take part in experimental design, experimentation, presentations, and preparation of publications. The successful candidates will be expected to actively seek funding with the assistance of the PI.
What do we offer?
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 better health for all. At Karolinska Institutet, we conduct innovative medical research and provide the largest range of biomedical education in Sweden. Karolinska Institutet is a state university, which entitles employees to several benefits such as extended holiday and a generous occupational pension. Employees also have free access to our modern gym and receive reimbursements for medical care.
Welcome to apply at the latest 2021-08-19.
An employment application must contain the following documents in English or Swedish:
1. A complete curriculum vitae, previous academic positions, academic title, current position,
academic distinctions, and committee work
2. A complete list of publications
3. A summary of current work (no more than one page)
The application is to be submitted through the Varbi recruitment system.
Want to make a difference? Join us and contribute to better health for all