Fellowship for PhD in Medical Sciences, Work in Neuroscience with UNIVERSITY OF NICOSIA


The doctorate in medical sciences:

The Faculty of Medicine at the University of Nicosia offers the degree of Doctor of Medical Sciences. The degree is awarded to students who successfully complete an independent research program that innovates in their chosen field of study. The PhD program aspires to empower students to become independent researchers, thereby advancing innovation and development.

The research project:

We are currently inviting applications through a competitive process for high caliber candidates to apply for a PhD scholarship in the fields of Neuroscience and Biomedical Engineering. The successful candidate will enroll in the Doctor of Medical Sciences program and work under the supervision of Dr. Nicoletta Nicolaou with expertise in the fields of Neuroscience and Biomedical Engineering at the Faculty of Medicine, University of Nicosia.

Project description:

Title of the research project: Development of a closed-loop controller for the automatic delivery of anesthetic and analgesic agents during surgery using machine learning methods.

Background and rationale: The current practice of anesthesia during surgery consists of administering a cocktail of drugs (anaesthetics, analgesics, muscle relaxants) to achieve the desired state of surgical anesthesia. During surgery, the patient is connected to a number of sensors that monitor vital signs (eg cardiovascular parameters, respiration, etc.). The anesthesiologist monitors these vital signs (visually on the monitoring device) and manually adjusts the dosages of the various agents (anaesthetics, analgesics, muscle relaxants). In this open-loop approach, the anesthetist is effectively the one who manually closes the loop. The disadvantages of this open-loop approach are primarily related to the fact that the anesthesiologist monitors vital signs and must make judgments based on these visual observations as to whether or not adjustments are necessary to the dosages of agents administered. These vital signs provide clues to the patient’s underlying condition, but they are not considered reliable indicators of the underlying level of consciousness or depth of anesthesia.

In a closed-loop system, the loop is closed automatically: the patient’s condition is estimated from the patient’s vital signs, and agent dosages are automatically adjusted by the device. The anesthesiologist is not part of the automated closed loop, but always has the option of bypassing this automation and intervening manually. Closed loop (CL) systems provide better stability of cardiovascular parameters (longer heart rate duration and mean arterial pressure control), better performance and faster recovery compared to open loop systems. The development of a CL anesthetic delivery system is a very complex process that must integrate information from a number of biological signals from the central and autonomic nervous systems. To date, there are only a handful of CL systems that have been developed, but are not yet routinely available for commercial use in routine surgery.

Goals and Objectives: In this doctoral research project, a CL system for automatic agent delivery during surgery under general anesthesia will be developed and simulated using machine learning methods. The system will use characteristics of the central and autonomic nervous systems (CNS and ANS respectively) to distinguish between consciousness, anesthesia and the different levels of anesthesia (light, surgical, deep). The system will provide an enhanced anesthetic experience that will be individualized, leading to a better experience (e.g., maintenance at the level of surgical anesthesia, stability in cardiovascular activity, less recovery time, minimal side effects from overanesthesia, discharge faster from the hospital).

The main goals and objectives of this doctoral research project are:

1. Characterize the relationships between real brain data and cerebro-cardiovascular data recorded during surgeries under general anesthesia using machine learning methods, as well as the relationships between these physiological signals and the concentration of anesthetic agents and painkillers.

2. Develop a closed-loop controller that uses the developed machine learning models to automatically change the volume of anesthetics and analgesics to achieve and maintain a desired level of (un)consciousness.

3. Develop a simulation that associates an observed or desired anesthetic state with specific anesthetic and analgesic dosages.

4. Test the performance of the developed machine learning controller on automatic modification of anesthetic and analgesic dosages to maintain a desired level of (un)consciousness as defined in the simulated data.

The scholarship:

The scholarship will be for three to four years and will cover:

  • Tuition fees for the PhD program which are 13,500 in total for the first 3 years and 1,500 for year 4.

Requirements and Qualifications:

  • Eligible applicants must hold (or hold by the time the program is expected to start i.e. October 2022) a recognized degree (BSc or equivalent for entry to a Masters) and Masters (MSc) in the field(s) in Neuroscience and/or Biomedical Engineering and/or Computer Science, or a Doctor of Medicine (e.g. MBBS or MD). Programming skills (e.g. MATLABRegistered Trademark, Python) would be a plus.

Application for the doctoral scholarship:

Candidates must submit an online application via this link and upload the following supporting documents:

  • A cover letter clearly stating that they are applying for the PhD scholarship in the fields of Neuroscience and Biomedical Engineering for the doctoral research project Development of a closed-loop controller for the automatic delivery of anesthetic and analgesic agents during surgery using machine learning methods.
  • Copies of applicant’s qualifications/degrees, application can be assessed with scanned copies, but certified copies must be provided if applicant is successful and prior to enrollment in PhD programme.
  • Copies of applicant’s transcript(s) – application may be assessed with scanned copies, but certified copies must be provided if applicant is successful and prior to enrollment in the PhD program.
  • Proof of English language proficiency such as IELTS with an overall score of 7 and a minimum score of 7 in writing or TOEFL iBT with an overall score of 94 and a minimum score of 27 in writing. Other internationally recognized English language qualifications may be taken into consideration during the exam. Students from the UK, Ireland, USA, Canada (English speaking provinces), Australia and New Zealand are exempt from the English language requirement.
  • Two reference letters, at least one of which must be from an academic.
  • A complete Curriculum Vitae (CV).

Applications must be submitted by Friday July 29, 2022 at 5 p.m.. Please use Ref. Num. B2 next to your last name when you start your application, for example, Name Last name B2.

Only duly completed applications, containing all the necessary supporting documents, will be considered. Only shortlisted candidates will be contacted and invited for an interview.


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