We are seeking a highly motivated, enthusiastic, passionate, and communicative researcher, with a proactive and creative attitude who is eager to explore innovative solutions. If you recognize yourself in the story below, then you have the profile that fits the project and the research group:
1. I have a Master's degree in Computer Science, Artificial Intelligence, Electrical Engineering, Mechanical Engineering or a related field and performed above average in comparison to my peers.
2. I am proficient in written and spoken English.
3. During my courses or prior professional activities, I have gathered experience with machine/deep learning, and can demonstrate a strong affinity with these fields. Prior experience with reinforcement learning, multi-objective optimization and/or the CAD/CAM process is a plus.
4. I am proficient in Python and am familiar with data science and machine/deep learning toolkits.
5. As a PhD researcher at KU Leuven, I perform research in a structured and scientifically sound manner. I read technical papers, understand the nuances between different theories and implement and improve methodologies myself.
6. Based on interactions and discussions with my supervisors and the colleagues in my team, I set up and update a plan of approach for the upcoming 1 to 3 months to work towards my research goals. I work with a sufficient degree of independence to follow my plan and achieve the goals. I indicate timely when deviations of the plan are required, if goals cannot be met or if I want to discuss intermediate results or issues.
7. In frequent reporting, varying between weekly to monthly, I show the results that I have obtained and I give a well-founded interpretation of those results. I iterate on my work and my approach based on the feedback of my supervisors which steer the direction of my research.
8. I feel comfortable to work as a team member and I am eager to share my results to inspire and being inspired by my colleagues.
9. I value being part of a research group which is well connected to the mechatronics industry and I am eager to learn how academic research can be linked to industrial innovation roadmaps.
10. During my PhD I want to grow towards representing the research group on project meetings or conferences. I see these events as an occasion to disseminate my work to an audience of international experts and research colleagues, and to learn about the larger context of my research and the research project.
We encourage candidates from diverse backgrounds and experiences to apply, as we believe that different perspectives contribute to better research and innovation.
Application Instructions for the PhD vacancy
To apply for this position, please use the online application tool and ensure that you submit the following documents in a single PDF file:
11. Motivation Letter: A letter (maximum 1 A4 page) addressing your strengths and qualifications in relation to the project.
12. Complete Academic CV: A detailed CV including information about your education, current position, work experience (if any), employment gaps (if any), interests, extracurricular activities, international experiences, and projects demonstrating your programming/software skills, background knowledge relative to the project and level of expertise.
13. List of Publications: If applicable, provide a list of your publications, including DOIs. Please do not include PDFs of the publications.
14. Copies of Diplomas: Include copies of your BSc and MSc degrees.
15. Transcript of Records: Provide transcripts for your BSc and MSc degrees. If you have not yet completed your Master's degree, include your available credits and scores, as well as a list of courses you are taking in the upcoming semester.
16. English Summary of Master Thesis: A summary of your master thesis in English (maximum 1 A4 page, or 2 pages max when including a figure).
17. Proof of English Language Proficiency: Documentation demonstrating your proficiency in English (TOEFL, IELTS, …), if available.
18. Reference Letter or Contact Details: A reference letter or the contact information for one reference who can provide a recommendation letter upon request.
Context
The process of creating a finished part from the CAD model or technical drawing of this part is commonly called the CAD-to-CAM process resulting in a work plan, which is then further used to produce the physical part. Due to the high complexity, this process is time-consuming and typically performed by an experienced operator/engineer. It includes the planning of different processing steps, e.g., cutting methods, machining patterns, and sequences. Selecting the tool, clamping strategy, cutting patterns and parameters requires the availability of experienced operators, which is a challenge today. Especially in the case of complex parts, CAD-to-CAM is a tedious manual process and often results in suboptimal designs, both in terms of material removal rate, path length, idle time, tool change frequency, and quality. A traditional Computer-Aided Design & Manufacturing (CAD-to-CAM) workflow assists this process but is limited to validating the outcome of the operator decisions.
PhD research project
The objective of this PhD is to contribute to the automated generation of work plans, for both existing and new parts to be produced in one clamping operation, in order to support the operators and indirectly the production planners/engineers. To this end, it will be investigated how a novel machine learning-based methodology leveraging reinforcement learning with human feedback and multi-objective optimisation can be realized to generate new and even improve existing work plans, to semi- automate the CAD-to-CAM process. To this end, 3 main data sources will be leveraged: (i) the product design data (CAD files), (ii) historical work plans and process information, including machining parameters, constraints, tools, and strategies (i.e., the selection of processing types and their sequence), and (iii) historic machining quality data. The focus is on the mainstream machining processes namely turning, milling, and drilling.
This PhD position is part of the Flanders Make Strategic Basic Research project AutoCAM, which intends to largely automate the generation of work plans, for both existing and new parts to be produced in one clamping operation, to support the operators and indirectly the production planners/engineers in industry. It offers the opportunity to perform the research in close collaboration with leading industry partners.
The M-Group at KU Leuven Bruges Campus is an interdisciplinary research team focusing on intelligent and dependable mechatronic systems, combining research expertise from the departments of Computer Science, Electrical Engineering and Mechanical Engineering. One of the key research tracks focusses on the application of Artificial Intelligence and Machine Learning in real-world industrial settings. The objective of this PhD position is to explore the use of reinforcement learning with human feedback and multi-objective optimization to automate the CAD-to-CAM process in machining processes. The successful candidate will be offered to opportunity to pursue a PhD in Computer Science at KU Leuven, and will also be embedded within the Declarative Languages and Artificial Intelligence (DTAI) lab (https://dtai.cs.kuleuven.be), which pursues excellence in an explicit and synergistic combination of fundamental and applied research on machine learning and artificial intelligence. Doctoral training is provided in the framework of the Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd). The PhD will be supervised by Prof. Mathias Verbeke.
The position will be hosted within the collaborative and internationally oriented research environment at KU Leuven, one of the world's leading universities (ranked among the top 100 globally). Founded in 1425, KU Leuven has been a center of learning for nearly six centuries and is Belgium’s highest-ranked university, as well as one of the oldest and most renowned universities in Europe. KU Leuven provides a truly international experience, high-quality education, world-class research, and cutting-edge innovation, having topped Reuters' ranking of Europe's most innovative universities for four consecutive years.
We offer:
19. A fully funded 4-year PhD scholarship (extendable to 4 years), with a remuneration package competitive with industry standards in Belgium, a country with a high quality of life and excellent health care system.
20. Ample occasions to develop yourself in a scientific and/or an industrial direction. Besides opportunities offered by the research group, further doctoral training for PhD candidates is provided in the framework of the KU Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd), known for its strong focus on both future scientists and scientifically trained professionals who will valorise their doctoral expertise and competences in a non-academic context.
21. Opportunities to collaborate in groundbreaking research and participate in international conferences.
22. Access to state-of-the-art infrastructure and a range of university benefits (health insurance, etc.).
23. A dynamic, passionate team of fellow PhD students and test engineers.
As a PhD candidate, you will be based at KU Leuven’s Bruges Campus (https://www.kuleuven.be/english/bruges), as part of a dynamic and interdisciplinary team of AI researchers, with access to state-of-the-art lab facilities to experimentally validate your findings in close collaboration with industrial partners.
The successful candidate will be encouraged to present their research at international conferences and national events, with a strong emphasis on publishing high-quality conference papers and journal articles. They will benefit from our robust international research and industrial network, which is actively involved in this project.
KU Leuven Campus Bruges, located in the magnificent medieval city of Bruges in West Flanders, offers a vibrant academic setting in close proximity to a network of companies. The campus features newly established labs to support both educational and research needs.