Karl-Johan Grinnemo
3 months ago
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PhD in Computer Science: AI, Cybersecurity, and Privacy at Karlstad University Karlstad University in Sweden
Degree Level
PhD
Field of study
Computer Science
Funding
Paid doctoral studentship at Karlstad University. The appointment is a fixed-term doctoral position, initially for one year with possible extensions, normally four years of full-time study and up to five years if teaching or other duties are included. Salary follows the standard doctoral student salary level and local collective agreement. The project is funded by the Swedish Knowledge Foundation.
Deadline
Expired
Country
Sweden
University
Karlstad University
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Keywords
About this position
Karlstad University is advertising a doctoral student position in Computer Science within the Privacy and Security (PriSec) research group at the Department of Mathematics and Computer Science. The position is part of SPLAIM (Security and Privacy of Locally-Run AI Models), a project funded by the Swedish Knowledge Foundation.
The research focus is on secure, private, and efficient deployment of local large language models (LLMs) in real organisational and business environments. The doctoral project covers topics such as local LLM solutions, supporting infrastructure, privacy-focused training and inference, retrieval-augmented generation (RAG), parameter-efficient fine-tuning, hardware-aware optimization, and benchmarking under realistic resource constraints.
The doctoral student will work closely with researchers in PriSec and collaborate with industrial partners on requirement analysis, prototype development, experimentation, and evaluation. This makes the position especially relevant for candidates interested in the intersection of computer science, artificial intelligence, cybersecurity, and privacy-preserving AI.
Eligibility: applicants must meet the general doctoral entry requirements and the specific requirements for third-cycle studies in Computer Science. A relevant Master’s degree in Computer Science or Computer Engineering, or equivalent qualifications, is required. The university particularly values experience in AI/ML, generative AI, LLMs, secure systems, privacy, data protection, and resource-constrained deployment. Strong English skills are expected; Swedish is an advantage.
Funding and terms: this is a paid doctoral studentship, normally four years full-time, with possible extension up to five years if teaching or other duties are included. Salary follows the standard doctoral student salary scale and local collective agreement.
Application deadline: 2026-07-31. Applications must be submitted via Karlstad University’s Varbi recruitment portal and should include a cover letter, CV, degree certificates and transcripts, thesis, publications or certificates, recommendation letters, and two references. Reference number: REK 2026/128.
Funding details
Paid doctoral studentship at Karlstad University. The appointment is a fixed-term doctoral position, initially for one year with possible extensions, normally four years of full-time study and up to five years if teaching or other duties are included. Salary follows the standard doctoral student salary level and local collective agreement. The project is funded by the Swedish Knowledge Foundation.
What's required
Applicants must meet the general doctoral entry requirements: a second-cycle qualification, at least 240 credits with at least 60 credits at second-cycle level, or equivalent knowledge. For the specific entry requirements in Computer Science, applicants must have a Master’s degree (60 credits) in Computer Science, a Master’s degree (120 credits) in Computer Science, or a Master of Science in Computer Engineering, or equivalent qualifications. Applicants must also be judged able to benefit from the programme. Strong preference is given to candidates with research or development experience in AI, machine learning, large language models, or generative AI; experience designing, implementing, deploying, optimizing, or evaluating AI/ML systems; knowledge of cybersecurity, privacy, secure systems, data protection, threat modelling, or privacy-preserving technologies; and familiarity with local LLM methods such as fine-tuning, RAG, quantization, model compression, benchmarking, or performance evaluation. Experience with requirement analysis, prototyping, user studies, interdisciplinary collaboration, independent work, and strong written and spoken English are also valued. Knowledge of Swedish, or willingness to learn Swedish, is an advantage.
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Professors
Senior Lecturer
Sweden
Research Interests
Internet Of Things
Cloud Computing
Mobile Networking
Wireless Sensor Network
5g Technology
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