and analysis tools such as Pandas, NumPy, Scikit-learn, etc. Knowledge of machine learning concepts such as supervised and unsupervised learning, classification, regression, clustering, dimensionalityreduction, etc. Experience with applying machine learning to real-world problems, such as aerospace engineering or another discipline in the sciences. Strong problem More ❯
Hands-on experience with classical ML and modern techniques, including deep learning , transformers , diffusion models , and ensemble methods . Solid understanding of feature engineering, dimensionalityreduction, model construction, validation, and calibration. Experience with uncertainty quantification and performance estimation (e.g., cross-validation, bootstrapping, Bayesian credible intervals). Familiarity with More ❯
Docker, and workflow managers such as Nextflow or Snakemake. Strong grounding in statistics and/or machine learning, including common approaches like clustering, classification, dimensionalityreduction, and regression modeling. Demonstrated experience working with one or more of the following data types: Single-cell and/or bulk transcriptomics More ❯
Docker, and workflow managers such as Nextflow or Snakemake. Strong grounding in statistics and/or machine learning, including common approaches like clustering, classification, dimensionalityreduction, and regression modeling. Demonstrated experience working with one or more of the following data types: Single-cell and/or bulk transcriptomics More ❯