Location : Delft (The Netherlands) & Lagos (Nigeria)

Function types : PhD position

PhD degree: issued by TU Delft (Delft University of Technology, The Netherlands)

Scientific fields : Engineering, Sciences

Hours : 38.0- 40.0 hours per week

Education :  University Graduate

Job Description

The INSPiRED project aims to develop easy-to-operate smart optical diagnostics devices for parasitic diseases which are integrated (include sample preparation and diagnosis), inclusive (cocreation with relevant stakeholders), and thoroughly tested in laboratory as well as field settings in endemic countries. The INSPiRED project is a collaboration between Leiden University Medical Center (LUMC), Delft University of Technology, University of Ibadan, University of Lagos, and CERMEL (see Annex 1 for project summary).

Currently, optical detection of light density early-stage parasitic infections requires the analysis of large volumes of sample material. Without computer-assisted analysis, an enormous amount of information is transferred by optical systems and processed by laboratory personnel. However, the information capacity of a microscope objective is physically limited by the laws of physics while processing of information is restricted by the natural limits of human abilities.

To increase information throughput, multi-modal imaging and digital holography are being developed in our optics laboratory in Delft.  For example, ptychographic imaging allows an order of magnitude increase of image information content by exploiting the higher harmonics of light scattered by samples.

The PhD will develop and optimise optical imaging techniques in collaboration with optical experts in Delft and industry. The methodology will be based on the combination of optics and data processing with high information throughput to generate  high-content images which  will be analysed using computational inverse scattering, neural networks and mathematical decision theory.


Prospective applicants should have a strong academic record in artificial intelligence, embedded system or relevant engineering field, with solid background in machine learning, computer vision and digital image processing. Good software development and programing skills are expected, preferably in python/MATLAB.  Knowledge of deep-learning frameworks(TensorFlow/Caffe) and OpenCV is a plus. A certain affinity towards turning complex theoretical concepts into real-world practise (developing proof-of-concept & prototypes) is desired.  Candidates should have a minimum of of 2nd class upper for their BSc (2nd class lower with very strong practical experiences will be considered). All applicant(s) are expected to be able to act independently as well as to collaborate effectively with members of a larger team. Good English skills are required.

Research will be performed in collaboration with University of Lagos where the candidate is expected to work and spend most of the PhD years .

Conditions of employment

TU Delft offers a monthly bursary to successful PhD candidate who will be resident in Lagos Nigeria. As a PhD candidate, you will be enrolled in the TU Delft Graduate School and you will be required to visit TU Delft to complete your graduate school courses during your PhD program. TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Program aimed at developing your transferable, discipline-related and research skills. Please visit more information.

Contract type: Temporary, 4 years.


Employer – Delft University of Technology

Delft University of Technology (TU Delft) is a multifaceted institution offering education and carrying out research in the technical sciences at an internationally recognised level. Education, research and design are strongly oriented towards applicability. TU Delft develops technologies for future generations, focusing on sustainability, safety and economic vitality. At TU Delft you will work in an environment where technical sciences and society converge. TU Delft comprises eight faculties, unique laboratories, research institutes and schools.


Department – Faculty Mechanical, Maritime and Materials Engineering

The 3ME Faculty trains committed engineering students, PhD candidates and post-doctoral researchers in ground breaking scientific research in the fields of mechanical, maritime and material engineering. 3mE is the epitome of dynamic, innovative faculty, with a European scope that contributes demonstrable economic and social benefits.

The principal foundation of the Numerics for Control & Identification(N4CI) team is the analysis of signals in the area of systems and control. The objective of our research is to extract crucial information from signals in order to understand and diagnose systems, to identify and model their dynamics, and to interact with them in order to reach a specific goal or to improve their performance. A particular focus of our research is on new algorithms that exploit specific features of dynamical systems, often based on physical insight. We have extended our expertise to the area of automated detection and diagnosis of parasitic infections in humans because we believe in doing science for the benefit of the people.  For more information, see: control/research/numerics-for-control-identification/optical-smart-malaria-diagnostics-osmd/


Academic team

Prof. Dr. Wellington Oyibo – University of Lagos

Prof. Dr. Gleb Vdovine – Delft University of Technology

Associate Prof. Lisette van Lieshout – Leiden University Medical Center

Associate Prof. Jan Carel Diehl – Delft University of Technology

Temitope Agbana – Delft University of Technology.


Additional information


To apply, please prepare:


  • A letter of motivation explaining why you are the right candidate,
  • a detailed CV,
  • a bachelor Second Class Upper or First Class degree
  • a complete record of Bachelor and Master courses (including grades)
  • softcopy of your Master’s Thesis ,
  • any publications, and a list of projects you have worked on with brief descriptions of your contributions (max 2 pages) and the names and contact addresses of two references.


All these items should be combined in one PDF documents. Application should be submitted by email to the following contact persons: /


Deadline submission: April 9th, 2019