- FACULTY/DEPARTMENT- Faculty of Electrical Engineering, Mathematics & Computer Science
- JOB TYPE- PhD
- SCIENTIFIC FIELD- Engineering
- HOURS PER WEEK- 38-40
- SALARY- € 2.541,00 – € 3.247,00
- DESIRED LEVEL OF EDUCATION- University graduate
- VACANCY NUMBER- TUD04214
Thinking fast and automatic vs. slow and deliberate (respectively System I and II) is a popular analogy when comparing data-driven learning to the good old-fashion symbolic reasoning approaches. Underlying this analogy lies the distinct capabilities of these systems, as well as their limitations. While data-driven learning (System I) has striking performance advantages over symbolic reasoning (System II), it lacks abilities such as abstraction, deliberation, comprehensibility, and contextual awareness. Symbolic reasoning, on the other hand, addresses those limitations but often falls behind data-driven learning when it comes to speedy, efficient, and automated decision-making. In light of these challenges by both systems, an emerging question arises as to “How to draw out the best of both systems and unify data-driven approaches with rule-based, symbolic, logical, and commonsense reasoning?”
We are currently looking for a highly motivated individual to explore this research question and pursue a PhD in knowledge utilization and reasoning for trustworthy machine learning. In particular, the selected candidate will have the following responsibilities and goals:
- Investigate hybrid approaches: This involves exploring the fusion of symbolic reasoning and statistical inference techniques to incorporate prior knowledge, logical rules, and constraints into machine learning models.
- Enhance the trustworthiness of machine learning models with knowledge utilization: Develop rigorous methods to enhance the reliability and robustness of decision-making, and extract interpretable explanations from the hybrid approaches, enabling users to understand the decision-making process behind the models’ outputs and ensuring transparency.
- Examine the theoretical aspects: Perform extensive theoretical analysis on the developed methods and establish safety guarantees and a level of confidence for practitioners
- Address scalability and efficiency: Design scalable algorithms that can handle complex tasks and computational demands, ensuring the practical application of the developed techniques.
- Conduct experiments and evaluations: Use diverse datasets to assess the performance, interpretability, and scalability of the proposed approaches. Compare them with state-of-the-art methods and conduct thorough analyses to validate the effectiveness and advantages of the developed knowledge reasoning models.
Our research environment offers a dynamic, stimulating, and diverse atmosphere, providing you with several opportunities to collaborate with experts in the field. You will work within the Pattern Recognition and Bioinformatics group (Pattern Recognition Bioinformatics) within the Department of Intelligent Systems, which includes researchers working on machine learning, pattern recognition, computer vision, and socially perceptive computing. In particular, you will be advised by Nezihe Merve Gürel (https://nezihemervegurel.github.io/).
We are looking for a candidate that largely meets the following criteria:
- Solid background in linear algebra, probability theory, and statistics and ability to apply mathematical concepts to model and analyze machine learning algorithms.
- A master’s degree (or about to have) in statistics, mathematics, machine learning, artificial intelligence, computer science, or a related field.
- Experience in at least one of the following programming languages: Python, C, C++, C#
- Proficiency in spoken and written English
The following skills are advantageous to have:
- Experience with theorem proving
- Familiarity with automated, symbolic, or commonsense reasoning techniques
- Familiarity with popular machine learning libraries and frameworks such as Transformers-Huggingface, TensorFlow, PyTorch, or Scikit-learn.
We encourage you to apply even if you feel you don’t meet all the criteria above, as long as you are willing to acquire the complementary skills.
Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements.
Conditions of employment
Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.
Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2541 per month in the first year to € 3247 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.
The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged.
For international applicants, TU Delft has the Coming to Delft Service. This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme for partners and they organise events to expand your (social) network.
TU Delft (Delft University of Technology)
Delft University of Technology is built on strong foundations. As creators of the world-famous Dutch waterworks and pioneers in biotech, TU Delft is a top international university combining science, engineering and design. It delivers world class results in education, research and innovation to address challenges in the areas of energy, climate, mobility, health and digital society. For generations, our engineers have proven to be entrepreneurial problem-solvers, both in business and in a social context.
At TU Delft we embrace diversity as one of our core values and we actively engage to be a university where you feel at home and can flourish. We value different perspectives and qualities. We believe this makes our work more innovative, the TU Delft community more vibrant and the world more just. Together, we imagine, invent and create solutions using technology to have a positive impact on a global scale. That is why we invite you to apply. Your application will receive fair consideration.
Challenge. Change. Impact!
Faculty Electrical Engineering, Mathematics and Computer Science
The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) brings together three scientific disciplines. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors of the future, whilst also setting the foundations for the software technologies to run on this new generation of equipment – which of course includes AI. Meanwhile we are pushing the limits of applied mathematics, for example mapping out disease processes using single cell data, and using mathematics to simulate gigantic ash plumes after a volcanic eruption. In other words: there is plenty of room at the faculty for ground-breaking research. We educate innovative engineers and have excellent labs and facilities that underline our strong international position. In total, more than 1000 employees and 4,000 students work and study in this innovative environment.
Click here to go to the website of the Faculty of Electrical Engineering, Mathematics and Computer Science.
For more information about this vacancy, please contact Nezihe Merve Gürel at firstname.lastname@example.org.
Are you interested in this vacancy? Please apply before 31 August 2023 via the application button and upload:
- Your cover letter (max 2 pages) in which you describe your research interests and background and how they relate to the position. Please describe potential research projects you would like to pursue and share why you are motivated to work on knowledge utilization for machine learning.
- A curriculum vitae
- Transcripts of of records (both BSc and MSc)
- Names and contacts of at least 2 references (no letter is needed at this point)
- A pre-employment screening can be part of the selection procedure.
- You can apply online. We will not process applications sent by email and/or post.
- Please do not contact us for unsolicited services.