Job Information
Organisation/Company: Tallinn University of Techmology Department: Computer Systems Department Research Field: Computer science » Computer systems; Computer science » Computer hardware; Computer science » Computer architecture Researcher Profile: First Stage Researcher (R1) Positions: PhD Positions Application Deadline: 16 Sep 2026 - 00:00 (Europe/Tallinn) Country: Estonia Type of Contract: Temporary Job Status: Full-time Hours Per Week: 40 Is the job funded through the EU Research Framework…
Programme?: Other EU programme Is the Job related to staff position within a Research Infrastructure?: Yes
Offer Description
Tallinn University of Technology (TalTech) invites applications from highly motivated candidates with a Master's degree in Computer Engineering, Computer Science, Artificial Intelligence, or a closely related field for a fully funded PhD position in Security of Efficient AI Transformers.
Transformer-based models, including Large Language Models (LLMs), Vision Transformers (ViTs), and Vision-Language Models (VLMs), have become central to modern artificial intelligence. Their rapidly increasing computational and memory requirements make efficient deployment increasingly important, particularly on resource- and energy-constrained platforms. Techniques such as quantization, pruning and sparsity, KV cache, compression, and approximate computing are therefore widely used to reduce computational cost, memory footprint, latency, and energy consumption. These optimization techniques are usually evaluated in terms of accuracy, performance, memory usage, and energy efficiency, while their impact on the reliability and hardware security of Transformer-based systems remains less well understood.
Efficiency optimizations can fundamentally change how a Transformers respond to hardware faults and attacks. Reducing numerical precision can alter how corrupted values propagate through the model, while pruning and sparsity can change the redundancy available to tolerate errors. KV cache, compression and approximation can similarly affect the persistence and impact of corrupted data. Optimized implementations also introduce auxiliary structures, such as scale factors, zero-points, sparsity indices, metadata, and control information, whose corruption may have a disproportionately large effect on the final output.
This PhD project will investigate the hardware security and reliability in optimized Transformer-based AI systems. State-of-the-art optimization techniques will be applied to representative Transformer architectures and accelerators, and their impact on system resilience will be systematically characterized. The research will explore different attacks such as Side Channels attacks, Fault attacks and Hardware Trojans as well as reliability threats such as soft errors to investigate the vulnerability of different Transformer components.
A central objective is to develop lightweight protection and mitigation mechanisms that preserve the efficiency benefits of optimized Transformer inference. The project will explore the trade-offs among security, reliability, model quality, performance, memory footprint, energy consumption, and hardware overhead, with the goal of developing methodologies and protection mechanisms for dependable, secure, and efficient Transformer inference.
Where to apply
E-mail: tara.ghasempouri@taltech.ee
Requirements
Research Field: Computer science
Education Level: Master Degree or equivalent
Skills/Qualifications
How to Apply
The successful candidate will work under the supervision of Prof. Tara Ghasempouri and Dr. Mohammad Hasan Ahmadilivani, and will collaborate with faculty members and researchers at Tallinn University of Technology (TalTech).
Please submit your CV and academic transcripts to tara.ghasempouri@taltech.ee and mohammad.ahmadilivani@taltech.ee using the subject line: “PhD Position – Secure, reliable and efficient transformer-based AI systems”. Candidates with suitable backgrounds will be invited for an online interview.
Application deadline: 16 September 2026
Expected starting date: As soon as possible (negotiable)
Salary/Funding: The PhD position is fully funded. The salary amount is determined according to the applicable TalTech salary table and university regulations.
Requirements
The following documents are necessary IF the candidate passed the first interview:
- Curriculum Vitae, including education, research experience, publications, and relevant technical skills.
- Copy of the identification page of the passport
- Bachelor's degree diploma and academic transcript.
- Master's degree diploma and academic transcript.
- Official English translations of qualification documents where required.
- Any additional documents required under the current TalTech doctoral admission regulations.
For current doctoral admission requirements, please visit:
https://taltech.ee/en/phd-admission
The following skills are mandatory:
- Strong background in machine learning and deep learning.
- Good understanding of Transformer architectures and modern AI models.
- Good programming skills, particularly in Python including experience with Pytorch.
- Good understanding of computer architecture and/or digital systems, and AI accelerators
- Strong written and spoken English.
- Ability to conduct independent research and work collaboratively.
The following skills are a plus:
- Experience with LLMs, Vision Transformers, or vision-language models.
- Familiarity with quantization, pruning, sparsity, or approximate computing.
- Knowledge of hardware security or hardware reliability
Languages: English
Level: Excellent
Additional Information
About TalTech
Founded in 1918, Tallinn University of Technology (TalTech) is Estonia's leading university of engineering and technology. Located in Tallinn, TalTech provides an international research environment bringing together expertise in information technology, engineering, artificial intelligence, cybersecurity, and digital technologies.
For more information, please visit:
For information about living and working in Estonia:
https://www.visitestonia.com/
https://www.workinestonia.com/
About Estonia
Estonia is internationally recognized for its advanced digital society and strong technology ecosystem. The country offers a highly connected environment for research and innovation, with active communities in artificial intelligence, cybersecurity, electronics, and digital technologies.
Tallinn combines a strong technology and research environment with a high quality of life, while Estonia offers extensive forests, lakes, coastline, and nature. Its international research community and advanced digital infrastructure provide an attractive environment for doctoral studies and technology research.
Website for additional job details: https://taltech.ee/en/
Work Location(s)
Number of offers available: 1
Company/Institute: Tallinn University of Technology
Country: Estonia
Contact
City: Tallinn
Website: https://taltech.ee/
Street: Akadeemia tee
E-Mail: tara.ghasempouri@taltech.ee, mohammad.ahmadilivani@taltech.ee
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