The MSCA SOPRANI network offers 10 full time PhD projects for masters in bio-informatics/mathematics/computer sciences/engineering/medicine/biomedical sciences.

جامعة حكومية
KU Leuven
المنطقة
أوروبا الغربية
البلد المضيف
بلجيكا
آخر موعد للتقديم
المستوى التعليمي
الدكتوراه
نوع الفرصة
الدكتوراه
التخصصات
تكنولوجيا المعلومات وعلوم الحاسوب
تمويل الفرصة
تمويل كامل
الدول المؤهلة
هذه الفرصة متوفرة لجميع البلدان
المنطقة المؤهلة
جميع المناطق

27 Jun 2023
Job Information
Organisation/Company

KU Leuven
Department

neurosciences
Research Field

Neurosciences » Neurophysiology
Medical sciences » Other
Engineering » Biomedical engineering
Computer science » Modelling tools
Researcher Profile

First Stage Researcher (R1)
Country

Belgium
Application Deadline

31 Dec 2023 - 23:59 (Europe/Brussels)
Type of Contract

Temporary
Job Status

Full-time
Is the job funded through the EU Research Framework Programme?

H2020 / Marie Skłodowska-Curie Actions
Reference Number

SOP-gen
Marie Curie Grant Agreement Number

101119916
Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Smart neurOmonitoring to support PRecision medicine in Acute central Nervous Injury

Description of the network:

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.

Responsibilities :

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

The project consists of the following PhD projects:

  • Unveiling and improving data on patients with acute CNS injury (VIB): 1. To make an inventory of existing data repositories; 2. To harmonize and standardize clinical, imaging and monitoring data originating from different devices, platforms and databases; 3. To develop and test a federated analysis network strategy that reconciles multicenter research with privacy legislation
  • Establishing insult burden curves for different types of secondary insults in acute brain injury (KU Leuven): 1. To establish insult burden curves for different types of secondary insults in acute brain injury; 2. To establish categorized insult burden curves for different types of patient and injury categories; 3 To develop smart visualization concepts of established monitoring signals and secondary insults
  • Multimodality monitoring in acute brain injury: what treatments have what effects? (JK University Linz): 1. To investigate effect of treatments on signals, insults and outcomes; 2. To capture and categorize domain knowledge on multimodality monitoring; 3. To develop a theoretical framework of current knowledge on multimodality monitoring and treatment algorithms; 4. To develop an ontology of pathophysiological events and proposed decisions
  • Associations between physiological and metabolic parameters and neurological outcome in SCI (Saint-George University London): 1. To associate multimodality monitoring signals and metabolics in SCI with short and long term outcomes; 2. To develop secondary insult burden curves in SCI
  • Multidimensional statistical disease model of traumatic brain injury (Leiden University Medical Center): 1. To build a statistical model using all available patient-, injury-, treatment- and outcome data from the different accessible datasets on traumatic brain injury and train the model to predict secondary insults and outcomes; 2. To explore and investigate hypotheses on treatment effects (in collaboration with ESR3); 3. To integrate relevant disease model concepts in a decision support platform
  • Multidimensional statistical disease model of acute stroke and subarachnoid haemorrhage (Leiden University Medical Center): 1. To build a statistical model using all available patient-, injury-, treatment- and outcome data from the different accessible datasets on stroke and subarachnoid hemorrhage and train the model to predict secondary insults and outcomes; 2. To explore and investigate hypotheses on treatment effects (in collaboration with ESR3); 3. To investigate collision opportunities for the statistical disease models on traumatic and non-traumatic acute brain injury; 4. To integrate relevant disease model concepts in a decision support platform
  • A dynamic cerebral autoregulation status monitor in a piglet cranial window model of severe TBI (KU Leuven): 1. To develop a dynamic monitor of CA in the piglet cranial window model; 2. To validate the dynamic CA monitor in a piglet model of severe TBI equipped with a cranial window; 3. To investigate (molecular) drivers of impaired CA
  • Clues for the monitoring of NV unit dysfunction in a translational setting (Charité Berlin): 1. To explicit relations between CA, hemodynamic responses to SD and blood-brain barrier function in an animal model of SD-induced spreading ischemia; 2. To explicit these relations in an animal model of primary vasospasm-induced ischemia; 3. To explicit these relations in a patient dataset of aneurysmal SAH documented with longitudinal MRI, subdural electrocorticography and neurovascular monitoring; 4. To investigate NV unit dysfunction in conjunction with CA dysfunction in a piglet cranial window model of severe TBI
  • Neuromonitoring derived metrics for the assessment of pathophysiological hemodynamics (University of Cambridge): 1. To integrate experimental animal data on cerebral hemodynamics into the ODE cerebral circulation model; 2. To use advanced signal processing methods to extract relevant hemodynamic parameters of the circulation model from high resolution monitoring signals obtained in the animal models; 3. To extract similar hemodynamic parameters from high resolution patient data from the accessible clinical data repositories; 4. To investigate relations between the statistical disease model (IRP 5 & 6) and the ODE cerebral circulation model
  • Effectiveness, cost-effectiveness and shortcomings of current neuromonitor technology and barriers to new technology (Leiden University Medical Center): 1. To investigate effectiveness of current neuromonitor technology in the management of acute brain injury by a systematic review; 2. To investigate cost-effectiveness of current neuromonitor technology based on 2 tertiary hospitals’ databases; 3. To adopt qualitative research methods to document shortcomings of current neuromonitor technology as well as practice variations and expert wishes; 4. To develop strategies for the testing of new neuromonitor concepts in prospective patient trials.

    • Requirements
    Research Field
    Computer science
    Education Level
    Master Degree or equivalent

    Research Field
    Engineering
    Education Level
    Master Degree or equivalent

    Research Field
    Medical sciences
    Education Level
    Master Degree or equivalent

    Research Field
    Mathematics
    Education Level
    Master Degree or equivalent

    Research Field
    Neurosciences
    Education Level
    Master Degree or equivalent

    Skills/Qualifications

    Profile :

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

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

    Proficiency in written and spoken English is crucial.

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

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


    Languages
    ENGLISH
    Level
    Good

    Additional Information
    Benefits

    The selected candidates are offered:

    • A full time PhD position in one of the 7 world-renowed research institutions and in the dynamic, multidisciplinary and intellectually challenging environment of the SOPRANI network, under supervision of and in close collaboration with experts from a wide variety of domains.
    • A thorough scientific education and training in all relevant competences to advance the field of neuromonitoring, enabling the possibility to become a world-class researcher in this field.
    • The possibility to actively participate in the network’s organizational structure and in international conferences and collaborations.
    • A predetermined living allowance, and when appropriate a mobility allowance, family allowance, long-term leave allowance or special needs allowance according to EU standards corrected for the country of employment and integrally transferred to the researchers.

    Additional comments

    For more information please contact

    Prof. Dr. Bart Depreitere, project coordinator

    Neurosurgery University Hospitals Leuven

    bart.depreitere@uzleuven.be

    and indicate what is your profile and which PhD you are interested in.

    The actual start of the research will be situated around Q2 2024.

    You can apply for this job no later than December 1 2023.


    Work Location(s)
    Number of offers available
    2
    Company/Institute
    KU Leuven
    Country
    Belgium
    Geofield


    Number of offers available
    1
    Company/Institute
    Johannes Kepler University
    Country
    Austria
    Geofield


    Number of offers available
    1
    Company/Institute
    Charité Berlin
    Country
    Germany
    Geofield


    Number of offers available
    3
    Company/Institute
    Leiden University Medical Center
    Country
    Netherlands
    Geofield


    Number of offers available
    1
    Company/Institute
    VIB
    Country
    Belgium
    Geofield


    Number of offers available
    1
    Company/Institute
    Cambridge University
    Country
    United Kingdom
    Geofield


    Number of offers available
    1
    Company/Institute
    Saint-George University London
    Country
    United Kingdom
    Geofield


    Where to apply
    E-mail

    bart.depreitere@uzleuven.be

    Contact
    City

    Leuven
    Website

    https://www.kuleuven.be/kuleuven
    Street

    Herestraat 49
    Postal Code

    3000

    STATUS: EXPIRED
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    طبق الأن ( المتقدمين 42)