PhD student Acutelines
The UMCG is one of the largest hospitals in the Netherlands and takes care of an adherence region of > 2.5 million inhabitants in the north of the Netherlands. As PhD candidate you will be appointed to Acutelines (Center for Acute Care) and will be part of an enthusiastic, multidisciplinary research group with different lines of research in the field of acute medicine. The Center for Acute Care conducts high quality patient care to approximately 25,000 patients per year. In the current translational research project, we will use multiplex PCR, RNA-sequencing and proteomics to expand fundamental knowledge about the pathophysiology of sepsis and develop clinical tools to facilitate early recognition. ecognition of early sepsis is challenging, but of utmost importance to allow timely initiation of adequate care. The discriminative performance of clinical tools to facilitate recognition of sepsis is very limited and consequently, identification of patients at risk for deterioration and in need of treatment escalation depends on physicians’ gut feeling. We foresee a major role for artificial intelligence employing molecular diagnostics (a.o. multiplex PCR) at the bed-side (i.e. point-of-care) combined with medical data to facilitate timely recognition of patients at risk of deterioration and support clinical decisions. Hereby, we expect to shorten time to diagnosis and improve outcomes for patients with sepsis, by allowing timely initiation of adequate care. PhD candidates are enrolled at the Graduate School of Medical Sciences (GSMS) of the University of Groningen. Since its foundation in 1614, the University of Groningen (RUG) has enjoyed an international reputation as a dynamic and innovative center of higher education offering high-quality teaching and research. Belonging to the best research universities of Europe and joining forces with prestigious partner universities and networks, the University of Groningen is truly an international place of knowledge. Groningen is a thriving university city set in quiet, spacious surroundings.
Sepsis is a life-threatening syndrome caused by a dysregulated host response to infection, leading to organ dysfunction. The global burden is astonishingly high: sepsis causes one in five deaths - more than twenty people per minute worldwide. Despite attempts to unravel its complex pathogenesis, exact mechanisms driving the extreme heterogenic clinical course remain unknown. Treatment is limited to antibiotics and supportive care, rather than targeting molecular causes. We propose to develop a personalized approach to clinical management of early sepsis based on identifying temporal changes in the patient-specific molecular fingerprint of sepsis. Combining molecular data with medical data will enable early detection of the nature and dynamics of the molecular fingerprint in early sepsis. The combined molecular and physiological assessment will reveal biomarkers predictive of the clinical course, which can be used to support clinical decision making. We will develop artificial intelligence models to stratify patients for personalized treatment and predict the clinical course. We expect this unique systematic analysis of molecular responses related to clinical response over time to generate key insights into the pathogenesis of sepsis, essential to advance this field and optimize future outcomes for patients with.
As PhD student you will work on developing and validating novel tools for the diagnosis and stratification to personalized treatment in early sepsis. Therefore, we are collecting high-quality big data involving demographic and medical data from the electronic health record, combined with vital parameters, electrophysiological waveforms measured by wearable devices and cross-omics data. In the current project, we will perform transcriptomic (i.e. RNA sequencing) and proteomic measurements, which will be integrated by cross-omics analysis to predict response to treatment and identify pathways driving organ failure in sepsis. The results will be validated for clinical use on a point-of-care multiplex PCR. We will integrate medical and molecular data using machine-learning approaches to identify patients with early sepsis at risk for deterioration, predict treatment response and guide clinical decisions.
You will closely work with experts from different disciplines within the UMCG (including Internal Medicine, Emergency Medicine, [Experimental] Pharmacology) and experts from industry, as well as experts in this field from other universities. Results will be presented at scientific meetings and published in journals.
You should be a creative and enthusiastic researcher with an MSc degree in Bioinformatics, Molecular Biology, Biomedical Sciences, Artificial Intelligence, Data Sciences or similar, with a clear interest in big data and an affinity with acute care and medical applications. Experience with -omics data analysis and machine learning are preferred, but not essential. Good social, communicative, collaborative and problem-solving skills are essential.
The UMCG has a preventive Hepatitis B policy. The UMCG can provide you with the vaccination, should it be required for your position.
In case of specific professions a 'Certificate of Good Conduct' is required.
Conditions of employment
This is a full-time appointment (36 hours a week) for four years starting from October 1st (negotiable) that should result in a PhD thesis.
Your salary will be a minimum of € 2.631,- gross per month in the first year and a maximum of € 3.336,- gross per month (scale PhD) in the final (4th) year, based on a full-time appointment. In addition, the UMCG will offer you 8% holiday pay, and 8.3% end-of-year bonus. The conditions of employment comply with the Collective Labour Agreement for Medical Centres (CAO-UMC).
For more information about this vacancy you may contact:
Hjalmar Bouma telefoonnummer 0031 6 2565 0573
Applying for a job
Please use the the digital application form at the bottom of this page - only these will be processed.
You can apply until 19 September 2022.
Within half an hour after sending the digital application form you will receive an email- confirmation with further information.
Digital application form