Technical practice
How would you design an AI system for personalized sustainability recommendations?
What to coverThe role involves designing and evaluating green recommendation systems. Suggested preparation: explain the data inputs, user model, recommendation approach, and how you would test both relevance and sustainability-related outcomes.
How would you clean textual, behavioral, contextual, and sustainability-related data?
What to coverThe posting names these data types as inputs for AI modeling. Suggested preparation: discuss missing values, inconsistent formats, noisy records, and checks that preserve the meaning of sustainability-related information.
How would you combine multimodal data when records do not align neatly?
What to coverThe role calls for preprocessing multimodal datasets. Suggested preparation: explain how you would match records across sources, handle absent modalities, and check whether combining them improves the model.
How would you model user preferences and behavior for personalized recommendations?
What to coverUser modeling, preference understanding, and behavior analysis are stated workflow goals. Suggested preparation: distinguish observed behavior from stated preferences and explain how you would assess whether a model captures useful differences between users.
How would you approach recommendations when little is known about a user?
What to coverSuggested preparation: treat limited user information as a possible challenge in the stated personalization work, not as a specified project requirement. Compare a simple non-personalized starting point with approaches that use available context, and describe how you would evaluate the trade-off.
When might NLP or an LLM help with this work, and when might a simpler approach suffice?
What to coverThe posting lists NLP and LLMs among relevant areas of knowledge, alongside recommendation systems. Suggested preparation: relate a method to the stated textual data and compare its evaluation needs, complexity, and suitability for the task.
How would you design an experiment to compare two recommendation models?
What to coverConducting experiments and analyzing results are core responsibilities. Suggested preparation: define a baseline, a comparison, appropriate data separation, and a way to determine whether an apparent improvement is credible.
How would you evaluate recommendation quality alongside sustainability goals?
What to coverThe role focuses on green recommendations and model evaluation. Suggested preparation: distinguish measures of recommendation usefulness from measures tied to the intended sustainability outcome, and explain what you would do if they conflict.
What would you investigate if an AI model performed well in experiments but poorly on new data?
What to coverThe posting calls for performance analysis and iterative improvement. Suggested preparation: check data quality, differences between datasets, and experimental design before changing the model; explain how each check would guide the next experiment.
How would you build an efficient, repeatable data-processing and experimentation workflow?
What to coverDeveloping efficient workflows is an explicit responsibility. Suggested preparation: describe how you would organize preprocessing, model runs, and result tracking so the research team can compare experiments and reproduce findings.
Behavioural practice
Tell me about a time you worked with people from different disciplines on a technical problem.
What to coverThe role involves collaboration with the PI and researchers across disciplines. Suggested preparation: use a real situation, your actions to establish shared understanding, and the resulting decision or outcome.
Describe a time you proactively resolved a difficult data or modeling problem.
What to coverThe posting emphasizes analytical problem-solving and addressing technical challenges. Suggested preparation: explain the situation, how you diagnosed and acted on the problem, and what the result taught you.
Tell me about a time you communicated research or technical results to an audience with different expertise.
What to coverThe role includes publications, technical reports, deliverables, and demonstrations. Suggested preparation: give a real situation, explain how you adapted the evidence and limitations for that audience, and state the result.
Describe a time team feedback changed your approach to an experiment or system.
What to coverThe posting calls for close research-team collaboration and iterative improvement of AI systems. Suggested preparation: identify the situation, the feedback and action you took, and what changed in the result.