Technical practice
Deep Learning Architectures for RNA Sequence Design
What to coverDiscuss the suitability of different deep learning architectures, such as Transformers or Graph Neural Networks, for RNA sequence design. Address how you would handle the constraints of RNA secondary structure prediction and property optimization within your model design.
Generative Modeling for Computational Drug Discovery
What to coverExplain your experience with generative models like VAEs, GANs, or Diffusion models in the context of drug discovery. How do you ensure the generated sequences are biologically plausible and satisfy specific therapeutic property constraints?
Handling High-Throughput Genomics Datasets
What to coverDescribe your approach to processing and curating large-scale genomics or high-throughput screening data. Focus on techniques for data normalization, handling batch effects, and ensuring data quality for downstream machine learning tasks.
Framework Proficiency: PyTorch, JAX, or TensorFlow
What to coverCompare the advantages of using PyTorch, JAX, or TensorFlow for research-intensive AI projects. Suggestion: Highlight your ability to implement custom loss functions or complex training loops relevant to biological sequence modeling.
Integrating AI Predictions with Wet-Lab Validation
What to coverHow do you bridge the gap between computational predictions and experimental validation? Discuss strategies for identifying high-confidence candidates for wet-lab testing and how you incorporate experimental feedback into iterative model refinement.
Biological Sequence Analysis Techniques
What to coverWhat specific bioinformatics tools or algorithmic approaches do you use for sequence alignment, motif discovery, or structural analysis? Explain how these traditional methods complement modern deep learning approaches in your research workflow.
Model Evaluation in AI4Science
What to coverBeyond standard metrics like accuracy or F1-score, how do you evaluate the scientific validity of your models? Suggestion: Discuss the importance of domain-specific metrics, such as binding affinity prediction or structural stability, in the context of RNA therapeutics.
Staying Current with AI4Science Advancements
What to coverHow do you keep up with the rapidly evolving field of AI for scientific discovery? Describe a recent paper or breakthrough in AI-driven biology that influenced your research methodology.
Behavioural practice
Collaborating in an Interdisciplinary Environment
What to coverDescribe a situation where you had to explain complex AI concepts to non-technical collaborators, such as wet-lab biologists or clinicians. How did you ensure effective communication and alignment on research goals?
Navigating Research Challenges in an Agile Setting
What to coverProvide an example of a time when a research project did not yield the expected results. How did you pivot your approach, and what steps did you take to maintain progress in an agile research environment?
Managing Conflicting Research Priorities
What to coverTell me about a time you had to balance multiple research tasks, such as model development, data curation, and manuscript preparation. How did you prioritize your work to meet deadlines while maintaining high scientific standards?
Contributing to Scientific Impact
What to coverDescribe your experience in preparing research for publication in top-tier journals or conferences. How do you ensure your findings are presented effectively to both academic and industry partners?