{"id":3271821,"url":"https://alion.io/job/scania-thesis-worker","title":"Thesis worker","company":{"id":445730,"name":"Scania","domain":"scania.com","url":"https://alion.io/company/scania-4","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career sitemap","truth_index":{"grade":"A","score":100,"open_postings":17,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":17,"computed_at":"2026-10-28T05:59:10Z"}},"role":"Early Careers","role_family":"Early Careers","seniority":null,"employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Södertälje, Sweden"],"countries":["SE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false}],"status":"live","first_seen_at":"2026-10-26T12:06:54Z","employer_posted_date":"2026-10-26","last_verified_at":"2026-10-28T07:10:48Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Master Thesis: Agentic AI for Intelligent Logistics\nBackground\nLogistics operations involve a continuous flow of decisions related to material availability, transport, packaging, suppliers and operational deviations. Many of these decisions are still coordinated manually across people, systems and organizational boundaries.\nScania Logistics is exploring how agentic AI can enable more autonomous logistics operations. Unlike traditional automation, an AI agent can gather information from multiple sources, evaluate alternatives, interact with systems and people, and take action within defined guardrails.\nThe ambition is not unrestricted automation. It is bounded autonomy, where humans define objectives, decision rights and constraints, while agents handle suitable operational decisions and escalate uncertain or high-impact situations.\nThesis objective\nThe objective of this thesis is to investigate how agentic AI can support or autonomously manage an operational logistics decision flow.\nTogether with Scania, the student will select a relevant logistics area based on business priority, available data and technical maturity at the start of the thesis. Potential areas may include inbound logistics, transport, packaging, supplier collaboration, material availability or deviation management.\nPossible research questions\nWhich operational logistics decisions are suitable for AI-assisted or autonomous execution?\nHow should responsibilities between humans and AI agents be designed?\nWhat evidence, confidence levels and guardrails are required before an agent may act?\nHow should an agent respond when information is incomplete, conflicting or uncertain?\nHow can the solution be designed for reuse across several logistics flows?\nHow should operational value, safety and autonomy be measured?\nExpected work\nThe thesis is expected to include:\nAnalysis of a selected operational logistics decision flow\nIdentification of suitable agent responsibilities and autonomy boundaries\nDesign and implementation of an agentic AI prototype\nTesting using relevant operational, historical or simulated scenarios\nEvaluation of business value, decision quality, risk and scalability\nRecommendations for further development and industrial implementation\nThe exact use case and technical scope will be defined together with the student when the thesis begins.\nDesired background\nWe are looking for a student or student pair within areas such as artificial intelligence, computer science, industrial engineering, logistics, operations research or a related field.\nExperience with AI agents, machine learning, optimization, software development or simulation is beneficial. 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