PhD Researcher in Statistical and AI-Driven Modelling (Additive Manufacturing, Remote)

Job summary

This PhD position focuses on the intersection of additive manufacturing, microstructural engineering, and advanced statistical/machine-learning modelling. The role aims to optimize metal microstructures during and after the additive manufacturing process through data-driven approaches.

Qualifications

• A two-year master's degree (120 ECTS points) or equivalent.
• Strong programming skills in Python and experience with Linux platforms.
• Background in Mechanical Engineering, Material Science, Applied Physics, or related fields.
• Interest in metallic materials and experience with engineering software tools (e.g., FEA, CFD, material modelling tools).
• Familiarity with HPC environments and ROCm tools is a plus.
• Exposure to statistical and AI-driven modelling through academic or professional experience.

Responsibilities

• Establish correlations and causal pathways for microstructure evolution during and post-additive manufacturing.
• Conduct exploratory data analysis and mathematical model building using statistical/AI tools.
• Analyze uncertainty quantification and design experiments for data collection.
• Work with multi-modal datasets, focusing on data fusion and transformation techniques.
• Generate and integrate synthetic data through numerical simulations and real experimental data.
• Collaborate with scientists and other researchers on experimental campaigns.
• Participate in a 3–6-month foreign research stay to acquire advanced skills.

Skills

• Strong analytical and problem-solving skills.
• Creative thinking in computational and programming contexts.
• Excellent communication and teamwork abilities.
• Fluent in English, both oral and written.

Education

• A master's degree in a relevant field is required.

Tools

• Proficiency in programming languages, particularly Python.
• Familiarity with engineering software tools such as Abaqus, Ansys, OpenFoam, and others relevant to the field.