Bjorn De Sutter

Professor at Ghent University

Ghent University
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Positions (2)

Publisher
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Bjorn De Sutter

University Name
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Ghent University

Ghent University, a leading European institution with over 15,000 staff and 85 departments, is offering a full-time PhD position in the Department of Electronics and Information Systems, specifically within the Computing Systems Lab (CSL). The CSL is renowned for its research on powerful, energy-efficient, and secure computing systems, focusing on innovative processor architectures, resource management, embedded systems, and security solutions. The System Software team, led by Professor Bjorn De Sutter, is seeking a doctoral fellow to advance research in software and systems security. This PhD project centers on developing scalable techniques for finding, mitigating, and fixing bugs and vulnerabilities in large code bases written in C, C++, and Rust. Unlike many existing tools that require domain-specific knowledge or are too resource-intensive for practical use, this research aims to create solutions that learn rules from the code base itself and are efficient enough for real-world development environments. Key research topics include: (i) learning-based bug detection in C/C++/Rust code bases, (ii) automatic bug fixing, and (iii) enhancing static analysis tools to handle very large code bases. The work will contribute to both academic knowledge and practical tools for industry adoption. As a PhD student, you will collaborate with a diverse team of professors, postdocs, and fellow PhD students, sharing infrastructure and datasets, and engaging in open science practices. You will present your findings at international conferences and publish in scientific journals. The position also includes limited teaching responsibilities as a course assistant. Eligibility requires a Master’s degree in Computer Science, Computer Science Engineering, Informatics, or a closely related field. Candidates must demonstrate strong technical skills in systems programming, operating systems, and compilers, with mandatory proficiency in C/C++. Experience with developer tools (debuggers, compilers, software analysis and rewriting), LLVM, fuzzing, Linux environments, and large open-source projects is highly valued. Applicants should be independent, analytical, communicative, and fluent in English. The fellowship offers a competitive, tax-free salary for up to 48 months, with the initial contract for 12 months renewable upon positive evaluation. Additional benefits include extensive training opportunities, 36 days of annual leave, bicycle allowance, and eco vouchers. Ghent University promotes equal opportunities and diversity in recruitment. To apply, submit your CV, three references, diploma and transcripts (if available), and a motivation letter to Prof. Bjorn De Sutter at [email protected] by January 31, 2026. For further details, visit the supervisor’s academic page or contact him directly. Applications received after the deadline will not be considered.

8 months ago

Publisher
source

Bjorn De Sutter

University Name
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Ghent University

Ghent University’s Department of Electronics and Information Systems invites applications for a full-time doctoral fellowship in the field of Machine-At-The-End (MATE) software protection. The position is based in the Computing Systems Lab (CSL), a dynamic research group within the Faculty of Engineering and Architecture, renowned for its work on secure, efficient, and innovative computing systems. The System Software team, led by Professor Bjorn De Sutter, will supervise the research, which focuses on developing advanced techniques to protect software from adversaries with full control over target machines. The research addresses urgent challenges in MATE software protection, including the lack of standardized assessment methods, decision support tools for software protections, and insights into attacker capabilities, especially in the era of AI. Potential research topics include decision support for software protection tool use, novel obfuscation techniques to counter AI-based reverse engineering, leveraging AI and large language models to optimize reverse engineering strategies, modeling attack efforts, and integrating protection techniques into industrial software development lifecycles. The work involves design science research, algorithm and model development, prototype implementation, and validation on real-world software samples. Collaboration with peers and open science practices are emphasized, and the research will culminate in a PhD degree. The position offers a full-time contract for up to 48 months, starting with a 12-month period extendable upon positive evaluation. The fellowship provides a competitive, tax-free salary (100% of the net salary of an AAP member, adjusted for family status and seniority) and comprehensive staff benefits, including training opportunities, generous holiday leave, bicycle allowance, and eco vouchers. The role also includes limited teaching assistant duties for one semester course. Applicants must hold a Master’s degree in Computer Science, Computer Engineering, Informatics, Information Engineering Technology, or a related field. Essential qualifications include strong technical proficiency, especially in C/C++ programming, and an affinity for systems programming, operating systems, and software hacking. Experience with compilers, software protection, reverse engineering, program analysis, and platforms like Linux, Android, or iOS is advantageous. Familiarity with AI and large language models is a plus. Candidates should demonstrate independence, analytical and communication skills, teamwork, and fluency in English. To apply, submit your CV, three references, diploma and transcripts (if available), and a motivation letter to Prof. Bjorn De Sutter at [email protected] by January 31, 2026. Late applications will not be considered. Ghent University is committed to equal opportunities and diversity. For further information, contact Prof. De Sutter or visit the academic profile page.

8 months ago

Articles (6)

Year:

2023

Research in medical imaging is hampered by a lack of programming languages that support productive, flexible programming as well as high performance. In search for higher quality imaging, researchers can ideally experiment with novel algorithms using rapid-prototyping languages such as Python. However, to speed up image reconstruction, computational resources such as those of graphics processing units (GPUs) need to be used efficiently. Doing so requires re-programming the algorithms in lower-level programming languages such as CUDA C/C++ or rephrasing them in terms of existing implementations of established algorithms in libraries. The former has a detrimental impact on research productivity and requires system-level programming expertise, and the latter puts severe constraints on the flexibility to research novel algorithms. Here, we investigate the use of the Julia scientific programming language in the domain of PET image reconstruction as a means to obtain both high performance (portability) on GPUs and high programmer productivity and flexibility, all at once, without requiring expert GPU programming knowledge. Using rapid-prototyping features of Julia, we developed basic and performance-optimized GPU implementations of baseline maximum likelihood expectation maximization (MLEM) positron emission tomography (PET) image reconstruction algorithms, as well as multiple existing algorithmic extensions. Thus, we mimic the effort that researchers would have to invest to evaluate the quality and performance potential of algorithms. We evaluate the obtained performance and compare it to state-of-the-art existing implementations. We also analyse and compare the required programming effort. With the Julia implementations, performance in line with existing GPU implementations written in the low-level, unproductive programming language CUDA C is achieved, while requiring much less programming effort, even less than what is needed for much less performant CPU implementations in C++. Switching to Julia as the programming language of choice can therefore boost the productivity of research into medical imaging and deliver excellent performance at a low cost in terms of programming effort.

Year:

2022

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2022

Year:

2020

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2020

Year:

2020

Collaborators (2)

LEONARDO REGANO

Assistant Professor

University of Cagliari

ITALY
View details

CATALDO BASILE

Assistant Professor

Politecnico di Torino

ITALY
View details
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