The MSCA SOPRANI network offers 10 full time PhD projects for masters in bio-inf [Belgium]

 

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The SOPRANI network consists of 7 European research institutions and 1 non-academic partner and has recently been granted a European MSCA Doctoral Training Network grant to train and guide 10 doctoral candidates in a 10 individual research projects that are intended to substantially advance the field of neuromonitoring in patients with acute brain injury in neuro-intensive care. Current measurable physiological signals only roughly represent ongoing pathophysiological processes. As a result, no therapeutic action has been shown to be beneficial in randomized patient trials in this patient group. The project goal is to prepare novel dynamic insult monitoring technologies and to develop improved decision support by integrating disease models and insult/treatment ontologies into smart multimodality monitor software. A parallel goal is to unite high level expertise in clinical, biomedical, statistical and engineering sciences into one network to boost the next generation of researchers to substantially advance the field of neuromonitoring. The network includes 3 relevant animal models and access to large (multi)center patient databases with injury, treatment & outcome data. Smart monitor platforms that aid precision medicine in acute central nervous system injury close to trials and future innovation leaders are expected results. The institutions involved in the network are: - KU Leuven, Belgium (Bart Depreitere, Geert Meyfroidt) - Leiden University Medical Center, the Netherlands (Ewout Steyenbergh, Wilco Peul) - Charité Berlin, Germany (Jens Dreier) - Joseph Kepler University Linz, Austria (Raimund Helbok) - University of Cambridge, UK (Peter Smielewski) - Saint-George University London, UK (Samira Saadoun, Marios Papadopoulos) - VIB (Leuven), Belgium (Alan Urban) - Moberg Analytics, USA (Dick Moberg) The network includes leading researchers and clinicians in the field of central nervous system injury, biotechnology, biostatistics and data sciences, who have decided to join forces by composing a multidisciplinary team to teach young researchers in multiple competences and apply these in their research.

Project

The brain and spinal cord too often still act as a black box, even withcurrent multimodality monitoring in 2023, still leaving clinicians unsure ofwhat is really happening inside her/his sedated patient, how to make sense ofthe different signals displayed on the monitor and what therapeutic action totake or not to take. The underpinning of monitored signal changes by pathophysiologicalevent knowledge obtained in the lab andcarefully compared with insights from modelling big data from patientrepositories is a first and indispensable step towards next generationmonitoring. The combination of such an integrative monitoring platform withsmart visualization concepts – that associate events with probabilistic outcomeprediction – and event/treatment ontology architectures, has a strong potentialfor truly beneficial decision support, and will make the black box more transparent.The main and ultimate impact is expected in improved patient outcomes followingacute central nervous system injury. Based on incidences of the involvedpathologies in Europe and the proportion requiring ICU care, it is roughlyestimated that this matter concerns. 320,000 patients per year in Europe. Every neuron saved from ischemic or apoptotic death can make adifference for the individual patient: the difference between either or notregaining consciousness, either or not having sufficient strength and coordinationto walk, either or not avoiding serious cognitive decline.

The project consists of the following PhD projects:

1. Unveiling and improving data on patients with acute CNS injury (VIB): 1.To make an inventory of existing data repositories; 2. To harmonize andstandardize clinical, imaging and monitoring data originating from differentdevices, platforms and databases; 3. To develop and test a federated analysisnetwork strategy that reconciles multicenter research with privacy legislation

2. Establishing insult burden curves for different types of secondaryinsults in acute brain injury (KU Leuven): 1. To establish insult burden curvesfor different types of secondary insults in acute brain injury; 2. To establishcategorized insult burden curves for different types of patient and injurycategories; 3 To develop smart visualization concepts of established monitoringsignals and secondary insults

3. Multimodality monitoring in acute brain injury: what treatments havewhat effects? (JK University Linz): 1. To investigate effect of treatments onsignals, insults and outcomes; 2. To capture and categorize domain knowledge onmultimodality monitoring; 3. To develop a theoretical framework of currentknowledge on multimodality monitoring and treatment algorithms; 4. To develop an ontology of pathophysiologicalevents and proposed decisions

4. Associations between physiological and metabolic parameters andneurological outcome in SCI (Saint-George University London): 1. To associatemultimodality monitoring signals and metabolics in SCI with short and long termoutcomes; 2. To develop secondaryinsult burden curves in SCI

5. Multidimensional statistical disease model of traumatic brain injury(Leiden University Medical Center): 1. To build a statistical model using all availablepatient-, injury-, treatment- and outcome data from the different accessibledatasets on traumatic brain injury and train the model to predict secondaryinsults and outcomes; 2. To explore and investigate hypotheses on treatmenteffects (in collaboration with ESR3); 3. To integrate relevant disease modelconcepts in a decision support platform

6. Multidimensional statistical disease model of acute stroke andsubarachnoid haemorrhage (Leiden University Medical Center): 1. To build astatistical model using all available patient-, injury-, treatment- and outcomedata from the different accessible datasets on stroke and subarachnoidhemorrhage and train the model to predict secondary insults and outcomes; 2. Toexplore and investigate hypotheses on treatment effects (in collaboration withESR3); 3. To investigate collision opportunities for the statistical diseasemodels on traumatic and non-traumatic acute brain injury; 4. To integraterelevant disease model concepts in a decision support platform

7. A dynamic cerebral autoregulation status monitor in a piglet cranialwindow model of severe TBI (KU Leuven): 1. To develop a dynamic monitor of CAin the piglet cranial window model; 2. To validate the dynamic CA monitor in apiglet model of severe TBI equipped with a cranial window; 3. To investigate (molecular) drivers ofimpaired CA

8. Clues for the monitoring of NV unit dysfunction in a translationalsetting (Charité Berlin): 1. To explicit relations between CA, hemodynamicresponses to SD and blood-brain barrier function in an animal model ofSD-induced spreading ischemia; 2. To explicit these relations in an animalmodel of primary vasospasm-induced ischemia; 3. To explicit these relations ina patient dataset of aneurysmal SAH documented with longitudinal MRI, subduralelectrocorticography and neurovascular monitoring; 4. To investigate NV unitdysfunction in conjunction with CA dysfunction in a piglet cranial window modelof severe TBI

9. Neuromonitoring derived metrics for the assessment of pathophysiologicalhemodynamics (University of Cambridge): 1. To integrate experimental animaldata on cerebral hemodynamics into the ODE cerebral circulation model; 2. Touse advanced signal processing methods to extract relevant hemodynamicparameters of the circulation model from high resolution monitoring signalsobtained in the animal models; 3. To extract similar hemodynamic parametersfrom high resolution patient data from the accessible clinical datarepositories; 4. To investigate relations between the statistical disease model(IRP 5 & 6) and the ODE cerebral circulation model

10. Effectiveness, cost-effectiveness and shortcomings of currentneuromonitor technology and barriers to new technology (Leiden UniversityMedical Center): 1. To investigate effectiveness of current neuromonitortechnology in the management of acute brain injury by a systematic review; 2.To investigate cost-effectiveness of current neuromonitor technology based on 2tertiary hospitals’ databases; 3. To adopt qualitative research methods todocument shortcomings of current neuromonitor technology as well as practicevariations and expert wishes; 4. To develop strategies for the testing of newneuromonitor concepts in prospective patient trials.

Profile

The candidates should havea strong academic record and a Masters diploma in the fields ofBio-informatics, Computer Science, Mathematics, Engineering, Medicine orBiomedical Sciences. Previous research experience is a plus. The candidatesshould not have a PhD. Depending on the project type, knowledge of data analysis,(bio)statistics, machine learning and associated programming skills orphysiology, biology, medicine, and lab sciences are essential.

The candidates should beable to work independently, take initiative, adopt critical judgment anddemonstrate ability to work in team. The candidates should be motivated to workwith and listen to experts with a clinical background, with a biomedicalsciences background ànd with biostatistical and engineering backgrounds. Theproject will include several network wide educational events and a secondment,for which travel, communication and social skills are required.

Proficiency in written andspoken English is crucial.

The candidates can be of any nationality, butmust not have resided or carried out his/her main activity in the country ofthe recruiting beneficiary for more than 12 months in the 3 years immediatelyprior to his/her recruitment.

The selected candidates are expected to writea doctoral thesis on their research after 4 years (when relevant, a 4thyear of research will be funded by the host institution).

Offer

The selected candidates are offered:

• A full time PhD position in one of the 7 world-renowed researchinstitutions and in the dynamic, multidisciplinary and intellectuallychallenging environment of the SOPRANI network, under supervision of and inclose collaboration with experts from a wide variety of domains.
• A thorough scientific education and training in all relevant competencesto advance the field of neuromonitoring, enabling the possibility to become aworld-class researcher in this field.
• The possibility to actively participate in the network’s organizationalstructure and in international conferences and collaborations.
• A predeterminedliving allowance, and when appropriate a mobility allowance, family allowance,long-term leave allowance or special needs allowance according to EU standardscorrected for the country of employment and integrally transferred to theresearchers.

Interested?

For more information pleasecontact

Prof.Dr. Bart Depreitere, project coordinator

NeurosurgeryUniversity Hospitals Leuven

bart.depreitere@uzleuven.be

and indicate what is yourprofile and which PhD you are interested in.

The actual start of theresearch will be situated around Q2 2024.

You can apply for this job nolater than December 1 2023.

You can apply for this job no later than December 31, 2023 via the online application tool

KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.

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