# Thesis Project – AI as an Engineering Aid at Sandvik

Sandvik is hiring a Thesis Project – AI as an Engineering Aid. Level rates it Little AI ●○○○; you can [apply on Sandvik](https://jobsbylevel.com/go/72b3e697-da0c-40ef-baaa-ae5f359c7a0d?utm_medium=ai-agent).

AI Level 1, AI centrality 13 out of 100. Sweden, Gimo.

## Details

- Company: [Sandvik](https://jobsbylevel.com/companies/sandvik)
- AI level: AI Level 1 (score 13 out of 100)
- Location: Sweden, Gimo
- Posted: October 7, 2026
- Apply: https://jobsbylevel.com/go/72b3e697-da0c-40ef-baaa-ae5f359c7a0d?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=thesis-project-ai-as-an-engineering-aid-at-sandvik-206f0b

## Description

Sandvik Coromant is a world-leading supplier of tools and tooling systems for metal cutting. Behind that sits a production environment with a large installed base of automation equipment – some of it still running on Adept robot controllers programmed in V+. This thesis project covers AI as an engineering aid when modernizing legacy Adept/V+ robot installations. This is a proof-of-concept project with honest premises. We do not believe AI can replace robot, PLC, and safety engineers in a control system migration, and we are not asking you to prove that it can. What we want to know is something more useful: how much of the manual groundwork involved in such a migration can AI take off our hands? Background and scope of the project Parts of our production still rely on obsolete Adept/V+ robot controllers, while the mechanical equipment has many years of useful life left. Modernization is often postponed because it requires engineers to interpret undocumented code, reconstruct system behavior, and create documentation and tests. Although the architectural differences make automatic translation unrealistic, AI could support this groundwork through analysis, documentation, code drafts, and test generation. Purpose The purpose of the project is to quantify, on a single small and well-defined part of a real V+ application, how much AI can contribute in four specific areas: Analysis – understanding and explaining existing V+ code, its structure, dependencies and behavior Documentation – producing the functional and technical documentation that is missing today Code drafts – generating first-draft code for the target platform for an engineer to review and complete Test generation – deriving test cases and verification criteria from the original application For each area we want a measured answer: how much of the work could AI do, what was the quality, how much rework did an engineer have to put in, and how much time was saved in total? What this project is not The goal is explicitly not a fully automated, production-ready conversion. Success is a clear, well-substantiated picture of where AI helps and where it does not and a working method we can reuse. A result showing limited benefit is a perfectly acceptable outcome. We are after an honest answer, not a positive one. Work description The project runs as a proof of concept in five phases. All verification is done virtually against a 3D model; no commissioning on physical equipment is involved. 1. Scoping and reference behavior Together with your supervisor, select one small and well-defined part of a V+ application – a single sub-sequence or program module – as the case for the whole project Establish a measurable reference behavior for that part – motions, I/O, sequence logic and interfaces to surrounding equipment. This is what every later result is judged against Define up front how AI contribution will be measured in each of the four areas, so the comparison is fair and repeatable 2. AI-assisted analysis Use two or three AI tools to interpret and explain the V+ code: structure, control flow, dependencies and implicit assumptions Assess correctness against the reference behavior – what did the tools understand, what did they miss, and what did they state confidently but wrongly? 3. AI-assisted documentation Generate functional and technical documentation of the selected part with AI support Evaluate how much of the output is usable as-is, how much needs editing, and how it compares with documenting the same code manually 4. AI-assisted code drafts and test generation Produce first-draft code for the target platform with AI support, intended for engineering review rather than direct use Generate test cases and verification criteria from the original application Document failure patterns systematically. Where the tools go wrong is at least as valuable a result as where they succeed 5. Verification against a 3D model, and recommendation Build a simplified kinematic 3D model of the

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