Overview

We are seeking an enthusiastic mathematician to conduct research, innovate, and further develop our existing mathematical framework.
Client:

Project Overview:
This project involves building a platform that leverages unbiased multi-omics data from a leading biobank to learn biological network structures using a Bayesian AI-driven approach, enhancing the speed and depth of de-risking drug discovery across oncology, neurology, and rare diseases.

Responsibilities:
  • Enhance and optimize existing learning algorithms while developing new algorithms.
  • Conduct original research and contribute innovative ideas to advance the field.
  • Document research findings and contribute to the team’s technical documentation.
Nice To Have:
  • Experience with numerical algorithms.
  • Familiarity with parallel computing frameworks, particularly MPI.
  • Knowledge of BLAS/LAPACK, GraphBLAS libraries.
Required Qualifications:
  • PhD in Applied Mathematics, Statistics, Computational biology, or related fields.
  • Strong foundation in statistical methods: likelihood-based inference methods, statistical scoring functions (BIC, AIC, MDL), knowledge of regression analysis and model fitting.
  • Deep understanding of Bayesian network theory, including structure learning and parameter estimation, model selection, and inference techniques.
  • Strong foundation in graph theory.
  • Proficiency with EM algorithms and their variants.
  • Desired Additional Skills
  • Proficiency with numerical algorithms.
  • Experience with parallel computing frameworks, particularly MPI.
  • Knowledge of BLAS/LAPACK, GraphBLAS.
Benefits:
  • PhD in Applied Mathematics, Statistics, Computational biology, or related fields.
  • Strong foundation in statistical methods: likelihood-based inference methods, statistical scoring functions (BIC, AIC, MDL), knowledge of regression analysis and model fitting.
  • Deep understanding of Bayesian network theory, including structure learning and parameter estimation, model selection, and inference techniques.
  • Strong foundation in graph theory.
  • Proficiency with EM algorithms and their variants.
  • Desired Additional Skills
  • Proficiency with numerical algorithms.
  • Experience with parallel computing frameworks, particularly MPI.
  • Knowledge of BLAS/LAPACK, GraphBLAS.
Note:

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