博士职位：可解释的跨实例学习

The Artificial Intelligence Lab in the Department of Computer Science at Vrije Universiteit Brussel (VUB) is looking for a motivated PhD researcher in Computer Science. You will join Confident Data-Driven Decision Support (CoRDS), a new Doctoral Network funded by the European Union under the Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks programme (Grant Agreement No. 2). The position is hosted at VUB under the supervision of Prof. Ann Nowé, with the University of Vienna as academic partner and OMP as industry partner. Project How can reinforcement learning combined with e.g. graph neural networks solve scheduling problems faster and better? How can we transfer knowledge between similar problem instances? How can we better understand how reinforcement learning methods generalize across similar optimization problems? If these questions excite you, and you have a solid ML and/or OR background, we invite you to apply for this PhD topic on explainable, across-instance learning. You will work at the intersection of machine learning and operations research. You will develop learning-based methods that generalize across problem instances and can be explained to the people who use them. You will be part of a multidisciplinary, international team of doctoral candidates and supervisors across the network. Responsibilities You will: • develop reinforcement learning methods based on structured neural networks for scheduling and other combinatorial optimization problems; • design methods that transfer knowledge across problem instances and stay robust under concept drift; • study and explain how reinforcement learning methods generalize in optimization settings; • develop explainability techniques that make learned solution strategies understandable to practitioners; • carry out a secondment at the University of Vienna with strong expertise in OR; • carry out a secondment at OMP to apply and refine your research in an industry environment, and help develop a public benchmark based on an optimization problem from OMP; • take part in the training and networking activities of the MSCA Doctoral Network; • publish results in leading conferences and journals and present your work internationally. Profile We are looking for a candidate with: • a Master’s degree in Computer Science or a related field (e.g. Mathematics, Engineering, Operations Research), preferably with a specialization in machine learning or optimization; • a strong interest in reinforcement learning, structured neural networks and combinatorial optimization; • good programming and computational skills; • enthusiasm for research that links AI method development with industrial applications; • a strong command of English, which is the only language requirement. Experience with reinforcement learning, deep neural networks, scheduling, operations research, transfer learning or explainable AI is an advantage. What we offer • A 4-year PhD position. The first 3 years are funded under the MSCA 2025 regulations: • a tax-free PhD bursary of €4010/month, from which social security contributions are deducted; • a mobility allowance of €710/month; • a family allowance of €660/month, if applicable. • The 4th year is funded by VUB under national regulations (FWO PhD scale). • Planned secondments at the University of Vienna and OMP. • VUB’s standard benefits package, including hospitalization insurance, access to sports and library facilities, and training opportunities. Eligibility MSCA mobility rules apply. You cannot apply for a position in a country where you have carried out your main activity (work, studies, etc.) for 12 months or more in the 36 months before the recruitment date. Applications that do not meet this rule will be discarded, because they are not eligible for funding. How to apply Apply here before Nov 30th, 2026 18:00 (Brussels time) by sending an e-mail to and . Please take your time with a detailed motivation letter. It should address the specific positions you are applying for and describe your expertise in operations research and machine learning, including any research experience you have.